Biomedical Informatics

Bioinformatics; biomedical informatics; genome informatics

Biography

Peter White, PhD, is the Riveschl Professor and chair of the Department of Biomedical Informatics at the University of Cincinnati College of Medicine, and division director of Biomedical Informatics at Cincinnati Children's. In these roles, he oversees informatics research and resources at both institutions, including academic, educational, data services, technology development, and research IT missions.

As co-director of the Center for Pediatric Genomics, he also serves in a leadership capacity for establishing enterprise-level solutions to genome-based precision medicine at Cincinnati Children’s. In his research career, Dr. White has explored the development and application of novel approaches for disease gene discovery, including identifying causative genes for neuroblastoma, ADHD, autism, and congenital heart defects. He has also developed innovative approaches for integrating and disseminating clinical, phenotypic, and molecular data to researchers for promoting discovery and hypothesis validation.

Dr. White has recently played a lead informatics role on a number of national network research programs, including the NCATS Clinical and Translational Science Award for UC and Cincinnati Children’s, the NICHD Newborn Screening Translational Research Network, the NHLBI Bench to Bassinet Program, the NHGRI Clinical Sequencing and Exploratory Research and IGNITE Consortia, and the Genomic Research and Innovation Network.

PhD: Washington University, St. Louis, MO, 1992.

Masino AJ, Dechene ET, Dulik MC, Wilkens A, Spinner NB, Krantz ID, Pennington JW, Robinson PN, White PS. Clinical phenotype-based gene prioritization: An initial study using semantic similarity and the Human Phenotype Ontology. BMC Bioinformatics. 2014 Jul 21;15:248.

Gai X, Xie HM, Perin JC, Takahashi N, Murphy K, Wenocur AS, D’arcy M, O’Hara RJ, Goldmuntz E, Grice DD, Shaikh TH, Hakonarson H, Buxbaum JD, Elia J, White PS. Rare structural variation of synapse and neurotransmission genes in autism. Molecular Psychiatry. 2012;17:402-411.

Elia J, Gai X, Hakonarson H, White PS. Structural variations in attention-deficit hyperactivity disorder. The Lancet. 2011;377:377-378.

Elia J, Gai X, Xie HM, Perin JC, Geiger E, Glessner JT, D’arcy M, deBeradinis R, Frackelton E, Kim C, Lantieri F, Muganga BM, Wang L, Rappaport EF, Grant SFA, Devoto M, Shaikh TH, Hakonarson H, White PS. Rare inherited structural variants found in attention-deficit hyperactivity disorder are preferentially associated with neurodevelopmental genes. Mol Psychiatry. 2010;15:637-646.

Shaikh TH, Gai X, Perin JC, Glessner JT, Xie H, Murphy K, O’Hara R, Casalunovo T, Conlin LK, D’arcy M, Frackelton EC, Geiger EA, Imielinski M, Kim CE, Medne L, Wang K, Annaiah K, Bradfield JP, Dabaghyan E, Eckert A, Onyiah CC, Ostapenko S, Otieno FG, Santa E, Shaner JL, Skraban R, Smith RM, Elia J, Goldmuntz E, Spinner NB, Zackai EH, Chiavacci RM, Grundmeier R, Rappaport EF, Grant SFA, White PS*, Hakonarson H*. High-resolution mapping and analysis of copy number variations in the human genome: A data resource for clinical and research applications. Genome Research. 2009;19:1682-90. (* Co-senior authors)

Grants

NHLBI Pediatric Translational Consortium Administrative Coordinating Center. Co-Principal Investigator. National Heart, Lung, and Blood Institute (NHLBI)/ National Institutes of Health (NIH). Nov 2015-Oct 2020.

Newborn Screening Translational Research Network Data Coordinating Center. Principal Investigator. Eunice Kennedy Shriver National Institute of Child Health and Human Development (NIH). Sep 2013-Sep 2018.

Faculty

Bruce J. Aronow, PhD Co-director, Computational Medicine Center

Bruce J. Aronow, PhD, works toward unraveling both the role and mechanism by which the functional capabilities of the human genome shape human health and our ability to adapt to stressful challenges. The lab’s current research focus is to find or support efforts to solve problems relevant to genomic medicine by developing, both independently and collaboratively, new algorithms, tools and methodologies in translational bioinformatics.
Visit the Aronow/Jegga Lab.

513-636-0263 bruce.aronow@cchmc.org

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Bruce J. Aronow, PhD

Co-director, Computational Medicine Center

Academic Affiliations

Professor, UC Department of Pediatrics

Phone 513-636-0263

Fax 513-636-2056

Email bruce.aronow@cchmc.org

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Visit the Aronow-Jegga Lab.

Biography

Dr. Aronow is a computational geneticist and developmental biologist. His group carries out analyses of many different kinds of data, develops algorithms, and builds websites and databases. These tools allow researchers from varying disciplines and backgrounds to analyze genetic and genomic data—either their own or that gathered from published sources—to better understand, model, and carry out new research about normal development and disease. His group is highly collaborative with clinical and basic researchers across a broad range of research projects encompassing many areas of biology and disease, including normal and abnormal development, in vivo and in vitro disease models, and large-scale clinical studies.

Data of interest includes genetic, genomic, proteomic, metabolomic, imaging, and therapeutic agent response measures. Recent areas of interest include large-scale clinical sample analyses, single cell-based dissection of developmental and disease tissues, and in vitro stem cell-based modeling of normal and disease-affected tissues including abnormal neurological, immunological, cardiac, and cancer tissues.

The lab’s recent efforts focus on predicting new therapeutic approaches based on disease mechanisms in the areas of inflammatory bowel disease, eosinophilic esophagitis, sickle cell anemia, cardiac development, and neurological and psychiatric diseases. His group is working on efforts to define the transcriptome of the developing kidney, lung and brain. They are using stem cell-derived cells and organoids to dissect mechanisms that underlie organ development and function as well as oncogenesis. They are also working to infer novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships.

BS: Chemistry, Stanford University, Stanford, CA, 1976.

PhD: Biochemistry, University of Kentucky, Lexington, KY, 1986.

Research Fellowship: Division of Basic Science Research, Cincinnati Children's Research Foundation, Cincinnati, OH, 1986-1989.

Olsson A, Venkatasubramanian M, Chaudhri VK, Aronow BJ, Salomonis N, Singh H, Grimes HL. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature. 2016 Aug 31;537(7622):698-702.

Bakeer N, James J, Roy S, Wansapura J, Shanmukhappa SK, Lorenz JN, Osinska H, Backer K, Huby AC, Shrestha A, Niss O, Fleck R, Quinn CT, Taylor MD, Purevjav E, Aronow BJ, Towbin JA, Malik P. Sickle cell anemia mice develop a unique cardiomyopathy with restrictive physiology. Proc Natl Acad Sci U S A. 2016 Aug30;113(35):E5182-91.

Kanisicak O, Khalil H, Ivey MJ, Karch J, Maliken BD, Correll RN, Brody MJ, J Lin SC, Aronow BJ, Tallquist MD, Molkentin JD. Genetic lineage tracing defines myofibroblast origin and function in the injured heart. Nat Commun. 2016 Jul 22;7:12260.

Sarangdhar M, Tabar S, Schmidt C, Kushwaha A, Shah K, Dahlquist JE, Jegga AG, Aronow BJ. Data mining differential clinical outcomes associated with drug regimens using adverse event reporting data. Nat Biotechnol. 2016 Jul 12;34(7):697-700.

Salomonis N, Dexheimer PJ, Omberg L, Schroll R, Bush S, Huo J, Schriml L, Ho Sui S, Keddache M, Mayhew C, Shanmukhappa SK, Wells J, Daily K, Hubler S, Wang Y, Zambidis E, Margolin A, Hide W, Hatzopoulos AK, Malik P, Cancelas JA, Aronow BJ, Lutzko C. Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium. Stem Cell Reports. 2016 Jul 12;7(1):110-25.

Anil Goud Jegga, DVM, MRes

Anil Goud Jegga, DVM, MRes, designs, develops and applies novel and robust computational approaches that will accelerate the diffusion of genomics into biomedical research and education and convert the genomics big data deluge into systematized knowledge to understand the molecular basis of disease.
Visit the Jegga Lab.

513-636-0261 anil.jegga@cchmc.org

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Anil Goud Jegga, DVM, MRes

Academic Affiliations

Associate Professor, UC Department of Pediatrics

Phone 513-636-0261

Fax 513-636-2056

Email anil.jegga@cchmc.org

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Visit the Jegga Lab.

Biography

Anil Jegga, DVM, MRes, is a biological and medically-oriented computational biologist. The mission of the Jegga Lab is to design, develop and apply novel and robust computational approaches that will accelerate the diffusion of genomics into biomedical research and education and convert the genomics data deluge into systematized knowledge to help us understand the molecular basis of disease. The lab continues with their focus on integration and mining of multiple sources of genomic, genetic and biomedical data to derive models for pathways and processes underlying development, disease and drug response. Independently and collaboratively, they have previously developed and published tools that allow biologists with minimal computational experience to integrate diverse data types and synthesize hypotheses about gene and pathway function in human and mouse. These tools are designed to answer several straightforward questions that biologists frequently encounter while trying to apply systems-level analyses to specific biological problems. His team is currently focusing on developing and implementing systems biology-based novel computational approaches to identify drug candidates for rare lung disorders. They are also working to integrate and mine genomic and compound screening-based big data to identify drug repositioning and novel drug candidates.

Master of Research: University of York, England, UK.

Master of Veterinary Science: College of Veterinary Science, Hyderabad, India.

Bachelor of Veterinary Science & Animal Husbandry: College of Veterinary Science, Hyderabad, India.

Zhang M, Zhu C, Jacomy A, Lu LJ, Jegga AG. The orphan disease networks. Am J Hum Genet. 2011;88(6):755-766.

Sardana D, Zhu C, Zhang M, Gudivada RC, Yang L, Jegga AG. Drug repositioning for orphan diseases. Brief Bioinform. 2011;12(4):346-356.

Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(Web Server issue):W305-311.

Jegga AG, Inga A, Menendez D, Aronow BJ, Resnick MA. Functional evolution of the p53 regulatory network through its target response elements. Proc Natl Acad Sci USA. 2008;105(3):944-949.

Michal Kouril, PhD Director, Research IT Services

Michal Kouril, PhD, collaborates with several Cincinnati Children's divisions on a number of innovative technology-related projects. He also oversees the group that supports the research IT needs of the Cincinnati Children's Research Foundation.

513-636-1302 michal.kouril@cchmc.org

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Michal Kouril, PhD

Director, Research IT Services

Academic Affiliations

Assistant Professor, UC Department of Pediatrics

Phone 513-636-1302

Fax 513-636-2056

Email michal.kouril@cchmc.org

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Biography

Dr. Kouril collaborates with several Cincinnati Children's divisions on a number of innovative technology-related projects. One notable collaboration is the five-year R01 grant with the Division of Behavioral Medicine and Clinical Psychology (Jennie Noll, PI). The project is monitoring online behavior of abused and non-abused adolescents to look for inappropriate and risky behavior. In addition, Dr. Kouril oversees the Cincinnati Children's Research IT group, which maintains petabyte-size storage in a number of performance tiers including the fastest SSD-based systems used for the most demanding applications, such as research data warehousing, virtual desktop infrastructure and some production servers. His team built out the research disaster recovery infrastructure to accommodate applications that are required from the business continuity perspective. In addition, they have expanded the computational cluster and added cutting-edge technology such as large graphics processing unit capability and high-core density teraFLOPS-speed Intel Phi cards.

PhD: Computer Science, University of Cincinnati, Cincinnati, OH, 2006.

Kirkendall ES, Kouril M, Dexheimer JW, Courter JD, Hagedorn P, Szczesniak R, Li D, Damania R, Minich T, Spooner SA. Automated Identification of Antibiotic Overdoses and Adverse Drug Events via Analysis of Prescribing Alerts and Medication Administration Records. J Am Med Inform Assoc. 2016 Aug 9.

Deleger L, Lingren T, Ni Y, Kaiser M, Stoutenborough L, Marsolo K, Kouril M, Molnar K, Solti I. Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research. J Biomed Inform. 2014 Aug;50:173-83.

Kirkendall ES, Kouril M, Minich T, Spooner SA. Analysis Of Electronic Medication Orders With Large Overdoses. Applied Clinical Informatics. 2014;5(1):25–45.

Deleger L, Li Q, Lingren T, Kaiser M, Molnar K, Stoutenborough L, Kouril M, Marsolo K, Solti I. Building gold standard corpora for medical natural language processing tasks. AMIA Annu Symp Proc. 2012;2012:144-53.

Biesiada J, Porollo A, Velayutham P, Kouril M, Meller J. Survey of public domain software for docking simulations and virtual screening. Hum Genomics. 2011 Jul;5(5):497-505.

Long (Jason) Lu, PhD

Long (Jason) Lu, PhD, uses quantitative approaches from disciplines such as computer science and applied mathematics to analyze biomedical big data with the goal of addressing fundamental questions in biology and improving human health. He is interested in deciphering the human genetic blueprint, modeling complex biological systems (such as biomolecular networks and pathways), analyzing biomedical images, and data mining on medical records.
Visit the Lu Lab.

513-636-8720 long.lu@cchmc.org

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Long (Jason) Lu, PhD

Academic Affiliations

Associate Professor, UC Department of Pediatrics

Phone 513-636-8720

Fax 513-636-2056

Email long.lu@cchmc.org

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Visit the Lu Lab.

Biography

Long (Jason) Lu, PhD, focuses on bringing quantitative approaches from disciplines such as computer science and applied mathematics to study the molecular mechanisms of human diseases. His expertise includes biomolecular network predictions and analysis, machine learning and statistical inference, genomic and transcriptomic sequence analysis, and medical image analysis.

Dr. Lu developed a network-based approach that combines proteomics experiments and computational predictions to discover high-density lipoprotein (HDL) subspecies and correlate them with cardiovascular protection function. His approach identified 38 candidate HDL subparticles. Further biochemical characterization of these putative subspecies may facilitate the mechanistic research of cardiovascular disease and guide targeted therapeutics aimed at its mitigation.

In studying pediatric brain disorders, Dr. Lu developed a set of novel algorithms for analyzing brain anatomical and functional MRI images. These algorithms will be important tools in aiding physicians in diagnosis and developing treatment plans. Dr. Lu has also introduced a new perspective to characterize gene essentiality from protein domains, which addresses the limitations of traditional gene-level studies of essentiality.

To identify such essential domains, he developed an Essential Domain Prediction (EDP) Model and presented the first systematic analysis on gene essentiality on the level of domains. Dr. Lu’s research accomplishments have been recognized nationally and internationally by serving on grant reviewer panels for the National Institutes of Health and the National Science Foundation in the United States, the Natural Sciences and Engineering Research Council of Canada (NSERC), French National Research Agency (ANR), and National Science Centre of Poland (NCN).

BS: Biotechnology/Bioengineering, Peking University, Beijing, China.

PhD: Biochemistry, specialized in Computational Molecular Biology, Washington University School of Medicine, St. Louis, MO.

Postdoc: Research Associate, Bioinformatics, Yale University, New Haven, CT.

View Publications

Gordon SM, Li H, Zhu X, Tso P, Reardon CA, Shah AS, Lu LJ, Davidson WS. Impact of genetic deletion of platform apolipoproteins on the size distribution of the murine lipoproteome. Journal of Proteomics. 2016;146:184-94.

Tan L, Holland S, Deshpande A, Chen Y, Choo D, Lu LJ. A semi-supervised SVM model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging. Brain and Behavior. 2015;5(12):e00391.

Li H, Gordon SM, Zhu X, Deng J, Swertfeger DK, Davidson WS, Lu LJ. Network-Based Analysis on Orthogonal Separation of Human Plasma Uncovers Distinct High Density Lipoprotein Complexes. J Proteome Res. 2015 Aug 7;14(8):3082-94.

Lu Y, Lu Y, Deng J, Peng H, Lu H, Lu LJ. A novel essential domain perspective for exploring gene essentiality. Bioinformatics. 2015 Sep 15;31(18):2921-9.

Chen Y, Mazlack L, Minai AA, Lu LJ. Inferring causal networks using Fuzzy Cognitive Maps and evolutionary algorithms with application to gene regulatory network reconstruction. Applied Soft Computing. 2015;37:667-679.

Jun Ma, PhD

Jun Ma, PhD, researches developmental processes at a quantitative and systems level. His team investigates fundamental mechanisms of development through a combination of quantitative experimental approaches and theoretical and simulation approaches.

513-636-7977 jun.ma@cchmc.org

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Jun Ma, PhD

Academic Affiliations

Professor, UC Department of Pediatrics

Phone 513-636-7977

Fax 513-636-4317

Email jun.ma@cchmc.org

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Molecular mechanisms of gene regulation and embryonic development

Biography

Research performed by Dr. Ma’s team focuses on understanding developmental processes at a quantitative and systems level. They aim to establish quantitative models—with predictive power—of how embryonic patterns emerge in a manner that is proportional to embryo size. They perform experimental studies to facilitate model building, and use models to make predictions for experimental validations. They use Drosophila (fruit fly) embryos for their studies. The research by Dr. Ma’s team was supported by grants from the National Institutes of Health and the National Science Foundation.

BS: Peking University, 1978-1982.

PhD: Harvard University, Cambridge, MA, 1983-1988 (degree awarded 1990).

Wu H, Manu Jiao R, Ma J. Temporal and spatial dynamics of scaling-specific features of a gene regulatory network in Drosophila. Nat Commun. 2015.

He F, Wei C, Wu H, Cheung D, Jiao R, Ma J. Fundamental origins and limits for scaling a maternal morphogen gradient. Nat Commun. 2015;6:6679.

Liu J, Ma J. Dampened regulates the activating potency of Bicoid and the embryonic patterning outcome in Drosophila. Nat Commun. 2013;4:2968.

Liu J, Ma J. Fates-shifted is an F-box protein that targets Bicoid for degradation and regulates developmental fate determination in Drosophila embryos. Nat Cell Biol. 2011 Jan;13(1):22-9.

He F, Wen Y, Deng J, Lin X, Lu LJ, Jiao R, Ma J. Probing intrinsic properties of a robust morphogen gradient in Drosophila. Dev Cell. 2008 Oct;15(4):558-67.

Keith Marsolo, PhD

Keith Marsolo, PhD, researches methods to characterize the quality and suitability of electronic health record (EHR) data, approaches to collect and extract research data from the EHR at scale, the design and instantiation of common data models to facilitate distributed research queries, and the development of informatics architectures and standards that can support multi-center learning health systems.
Visit the Marsolo Lab.

513-803-0333 keith.marsolo@cchmc.org

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Keith Marsolo, PhD

Academic Affiliations

Associate Professor, UC Department of Pediatrics

Phone 513-803-0333

Fax 513-636-2056

Email keith.marsolo@cchmc.org

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Visit the Marsolo Lab.

Biography

Dr. Marsolo’s research interests include methods to characterize the quality and suitability of electronic health record (EHR) data; approaches to collect and extract research data from the EHR at scale; the design and instantiation of common data models to facilitate distributed research queries; and the development of informatics architectures and standards that can support multi-center learning health systems.

Dr. Marsolo serves as faculty advisor for BMI Data Services, which provides services in these areas. He is currently building on several grants from the Agency for Healthcare Research and Quality to design and implement an EHR-linked registry architecture for ImproveCareNow, a 94-center quality improvement and research network that focuses on improving the care and outcomes of children with inflammatory bowel disease (IBD). He and his team are extending the platform to support a pragmatic clinical trial that is being funded by the Patient-Centered Outcomes Research Institute (PCORI). This pragmatic trial will serve as an initial use case for a recently funded grant from the Office of National Coordinator for Health Information Technology (ONC) to pilot the use of interoperability standards and embed case report forms in the EHR, decreasing the amount of time spend on double data entry during research study visits.

Other recent highlights include work on Phase I and II of PCORI’s National Patient-Centered Clinical Research Network (PCORnet), including a pediatric-focused Clinical Data Research Network (CDRN), and a Patient-Powered Research Network (PPRN) with ImproveCareNow. In addition, Dr. Marsolo is a co-investigator within the Distributed Research Network Operations Center of the PCORnet Coordinating Center, served as one of the co-chairs of the PCORnet’s Data Standards, Security and Network Infrastructure (DSSNI) Task Force during Phase I of the project, and is a member of the Data Committee as part of Phase II.

BS: Computer Science and Engineering, The Ohio State University, Columbus, OH, 2002.

MS: Biomedical Engineering, The Ohio State University, Columbus, OH, 2005; Computer and Information Science, The Ohio State University, Columbus, OH, 2006.

PhD: Computer and Information Science, The Ohio State University, Columbus, OH, 2007.

Marsolo K, Margolis PA, Forrest CB, Colletti RB, Hutton JJ. A Digital Architecture for a Network-Based Learning Health System – Integrating Chronic Care Management, Quality Improvement, and Research. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2015 Aug 17;3(1):1168.

Forrest C, Crandall W, Bailey C, Zhang P, Joffe M, Colletti R, Adler J, Baron H, Berman J, DelRosario F, Grossman A, Hoffenberg E, Israel E, Kim S, Lightdale JR, Margolis P, Marsolo K, Mehta D, Milov D, Patel A, Tung J, Kappelman M. Effectiveness of Anti-TNFα for Crohn’s Disease: Research in a Pediatric Learning Health System. Pediatrics. 2014 Jul;134(1):37-44.

Marsolo K. Informatics & operations – let’s get integrated. J Am Med Inform Assoc. 2013 Jan 1;20(1):122-4.

Marsolo K. In Search of a Data-in-Once, Electronic Health Record-Linked, Multicenter Registry— How Far We Have Come and How Far We Still Have to Go. EGEMS (Wash DC). 2013 Jan 17;1(1):1003.

Marsolo K, Corsmo J, Barnes MG, Pollick C, Chalfin J, Nix J, Smith C, Ganta R. Challenges in creating an opt-in biobank with a registrar-based consent process and a commercial EHR. J Am Med Inform Assoc. 2012 Nov-Dec;19(6):1115-8.

Yizhao Ni, PhD Member, Division of Biomedical Informatics

Yizhao Ni, PhD, develops machine learning, natural language processing (NLP), and information retrieval techniques to assist clinical decision making.

513-803-4269 yizhao.ni@cchmc.org

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Yizhao Ni, PhD

Member, Division of Biomedical Informatics

Academic Affiliations

Instructor, UC Department of Pediatrics

Phone 513-803-4269

Email yizhao.ni@cchmc.org

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Clinical informatics; natural language processing; machine learning (predictive modeling)

Biography

Yizhao Ni’s research interest lies in the development of machine learning, natural language processing (NLP) and information retrieval techniques to assist clinical decision making. His research is application-oriented and the overall objective is to improve the quality of health care by: providing more effective provisioning of usable data (efficiency); helping clinicians generate more objective clinical decisions (effectiveness); and providing more reliable proactive prediction of clinical outcomes (safety). To achieve these objectives, he collaborates with clinical providers, information service administrators and biomedical and computational scientists.

Dr. Ni’s team has participated in a variety of research projects, including the Electronic Medical Records and Genomics Network (eMERGE) project, and , medication safety in intensive care units (R01), detection of surgery cancellation (R21), and sustainable surveillance of diabetes (U18). He has active collaborations with the divisions of Emergency Medicine, Hospital Medicine, Center for Autoimmune Genomics and Etiology, Neonatology & Pulmonary Biology, Anesthesia, Psychiatry and Oncology at Cincinnati Children’s; and with Neurology and Rehabilitation Medicine in the UC College of Medicine.

In addition to his research, Dr. Ni is serving as a machine learning specialist in multiple quality improvement projects such as the safety and situation awareness project.

BSc: Xiamen University, Xiamen, PR China, 2005.

MSc: University College London, London, UK, 2006.

PhD: University of Southampton, Southampton, UK, 2010.

Post-doctoral: University of Bristol, Bristol, UK, 2012.

Certification: Epic Clarity Data Model, 2013.

Barzman D, Ni Y, Griffey M, Patel B, Warren A, Mossman D, Latessa E, Sorter M. A pilot study on developing a standardized and sensitive school violence risk assessment with manual annotation. Psychiatric Quarterly. 2016.

Ni Y, Beck AF, Taylor R, Dyas J, Solti I, Grupp-Phelan J, Dexheimer JW. Will they participate? Predicting patients’ response to clinical trial invitations in a pediatric emergency department. Journal of the American Medical Informatics Association. 2016;23(4):671-80.

Ni Y, Kennebeck S, Dexheimer JW, McAneney C, Tang H, Lingren T, Li Q, Zhai H, Solti I. Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department. Journal of the American Medical Informatics Association. 2015;22(1):166-178.

Li Q, Spooner SA, Kaiser M, Lingren N, Robbins J, Lingren T, Tang H, Solti I, Ni Y. An end-to-end hybrid algorithm for automated medication discrepancy detection. BMC Medical Informatics & Decision Making. 2015;15(1):37.

Ni Y, Wright J, Perentesis J, Lingren L, Deleger L, Kaiser M, Solti I. Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients. BMC Medical Informatics & Decision Making. 2015;15(1):28.

Grants

Improving Intensive Care Medication Safety through EHR-based Algorithms. Co-Investigator. National Library of Medicine. Oct 2015-Sep 2019.

John P. Pestian, PhD, MBA Director, Computational Medicine Center

John P. Pestian, PhD, MBA, advances the science of natural language processing and understanding in biomedical settings. Along with a growing list of collaborators, his lab has developed neuro-cognitive algorithms that enable computers to understand concepts and semantic relationships within clinical text. Pestian also directs the Computational Medicine Center, established in 2003 by a $28 million grant from Ohio's Third Frontier Project.
Visit the Pestian Lab.

513-636-1627 john.pestian@cchmc.org

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John P. Pestian, PhD, MBA

Director, Computational Medicine Center

Academic Affiliations

Professor, UC Department of Pediatrics

Phone 513-636-1627

Fax 513-636-2056

Email john.pestian@cchmc.org

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Visit the Pestian Lab.

Biography

Dr. Pestian's lab focuses on developing advanced technology for the care of neuropsychiatric illness. Using artificial intelligence, his team integrates analyses of trait and state characteristics for early identification of both neurological and psychiatric illness. His lab developed and implemented an automated, electronic health record surveillance system that processes clinician notes to identify epilepsy surgery candidates up to two years earlier than traditional approaches. The lab also focuses on earlier identification of individuals at risk of suicide, depression, and bipolar and anxiety disorders using verbal and non-verbal language.

Current projects include fusion of linguistic, acoustic, and visual cues that are being tested in selected Cincinnati Public Schools and Cincinnati Children’s clinics. Dr. Pestian and his lab have 18 issued patents and he is active in the entrepreneurial community. This activity has yielded over 500 jobs and one-half billion in revenue have been created. One invention, Processing Text With Domain-Specific Spreading Activation Methods, is a platform for neuropsychiatric research. Another, Optimization and Individualization of Medication Selection and Dosing, has been used for optimal mental health drug selection on more than 420,000 people. He and his colleagues have published more than 80 peer reviewed publications that focus on applied and translational sciences apropos to artificial intelligence.

He currently mentors five junior faculty, of which three have recently received funding from the National Institutes of Health (NIH). Dr. Pestian is an alumni of the NIH’s standing Study Section, Biomedical Library and Informatics Review Committee (BLIRC) of the National Library of Medicine, as well as the National Institute for Mental Health’s, Pathway to Independence (K99) study section.

BS: St. Francis College, Loreto, PA, 1981.

MA: University of Steubenville, Steubenville, OH, 1987.

PhD: Virginia Commonwealth University, Richmond, VA, 1994.

Pestian JP, Sorter M, Connolly B, Bretonnel Cohen K, McCullumsmith C, Gee JT, Morency LP, Scherer S, Rohlfs L; STM Research Group. A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial. Suicide Life Threat Behav. 2016 Nov 3.

Cohen KB, Glass B, Greiner HM, Holland-Bouley K, Standridge S, Arya R, Faist R, Morita D, Mangano F, Connolly B, Glauser T, Pestian J. Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning. Biomed Inform Insights. 2016 May 22;8:11-8.

Pestian JP, Grupp-Phelan J, Bretonnel Cohen K, Meyers G, Richey LA, Matykiewicz P, Sorter MT. A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department. Suicide Life Threat Behav. 2016 Apr;46(2):154-9.

Connolly B, Matykiewicz P, Bretonnel Cohen K, Standridge SM, Glauser TA, Dlugos DJ, Koh S, Tham E, Pestian J. Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals. J Am Med Inform Assoc. 2014 Sep-Oct;21(5):866-70.

Pestian JP, Matykiewicz P, Linn-Gust M, South B, Uzuner O, Wiebe J, Cohen KB, Hurdle J, Brew C. Sentiment Analysis of Suicide Notes: A Shared Task. Biomed Inform Insights. 2012 Jan 30;5(Suppl 1):3-16.

Qureshi F, Pestian J, Davis P, Zaritsky A. Effect of nebulized ipratropium on the hospitalization rates of children with asthma. N Engl J Med. 1998 Oct 8;339(15):1030-5.

Krishna M. Roskin, PhD

Krishna M. Roskin, PhD, develops tools and techniques to analyze the immune receptor (BCR and TCR) repertoires and uses that to study the human immune system focusing on autoimmunity and immunodeficiencies.

513-803-2902

Krishna M. Roskin, PhD

Academic Affiliation

Assistant Professor

Stanford University School of Medicine

Phone 513-803-2902

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Clinical

Translational medicine: immunodeficiencies; autoimmunity

Research

Computational immunology: antibody and TCR sequence analysis

Bioinformatics: large-scale biological data systems; reproducible science.

Biography

With the sequencing of human genome nearing completion, there was a lot of interest in applying comparative genomics approaches to help unlock the secrets of the human genome. Dr. Roskin worked with David Haussler as part of the Mouse Genome Sequencing Consortium. Their goal was to analyze the genomic differences between mammalian species to find regions of the human genome that are conserved not by chance but because of evolutionary constraint. To that pairwise analysis, Dr. Roskin added data from the Rat Genome Sequencing Consortium to allow triangulation of evolutionary events across the whole genome.

A major result in the Mouse genome paper was Dr. Roskin's estimate of the share of the human genome under purifying selection. He developed score functions to detect unusually conserved regions in the human genome by comparing it to the mouse. By looking at the distribution of conservation scores genome-wide compared to the scores of regions under neutral section, he estimated that 5% of human genome is under purifying selection. This percentage is much higher than the fraction of the genome that codes for proteins and spurred new interest in function non-coding elements of the human genome.

BA: Mathematics, University of California, Santa Cruz, CA.

BS: Computer Science, University of California, Santa Cruz, CA.

PhD: Computer Science, University of California, Santa Cruz, CA.

Nathan Salomonis, PhD

Nathan Salomonis, PhD, uses computational approaches to examine the interplay between diverse modes of gene regulation, including transcription, alternative splicing, and genomic variation. We strive to develop open-source computational genomics approaches that are accessible by both untrained and skilled computational biologists alike.

650-576-1646 nathan.salomonis@cchmc.org

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Nathan Salomonis, PhD

Academic Affiliation

Assistant Professor

UC Department of Biomedical Informatics

Phone 650-576-1646

Email nathan.salomonis@cchmc.org

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Bioinformatics; genomics; cancer genomics; single-cell RNA-Seq analysis; alternative splicing; pathway analysis; pathway visualization; pathway curation; SIDS; stem cell biology; cardiac specification; renal graft dysfunction

Biography

Dr. Salomonis and his group are on the cutting edge of developing new software and algorithms to identify complex functional relationships from whole transcriptome data. They have developed several open source analysis tools including AltAnalyze, LineageProfiler, GO-Elite, and NetPerspective. The advent of single-cell genomic profiles has created many new opportunities for understanding stochastic decisions mediating stem cell differentiation to distinct cell fates and the regulation of distinct gene expression and splicing programs. They are capitalizing on this new technology to explore these decision-making processes at a resolution never previously possible.

Last year, they worked collaboratively with a dozen investigative research teams within Cincinnati Children's to develop new methods for evaluating whole genome transcriptome datasets. These methods include: 1) the detection of distinct gene and splicing populations from bulk and single cell genome profiles, 2) predicting implicated cell types present in complex fetal maternal biological samples and 3) identifying new disease regulatory networks related to pediatric and adult cancers, cardiovascular disease and spinal cord injury.

BS: University of California, Los Angeles, CA, 1998.

PhD: University of California, San Francisco, CA, 2008.

Postdoctoral Fellow: Gladstone Institutes, San Francisco, CA, 2012.

View Pub Med Publications

Roedder S, Salomonis N. Loaded, locked, drawn: kSORT validated for patient samples. Nat Rev Nephrol. 2016 Nov 14.

Olsson A, Venkatasubramanian M, Chaudhri VK, Aronow BJ, Salomonis N, Singh H, Grimes HL. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature. 2016.

Sigdel TK, Bestard O, Salomonis N, Hsieh SC, Torras J, Naesens M, Tran TQ, Roedder S, Sarwal MM. Intragraft Antiviral-Specific Gene Expression as a Distinctive Transcriptional Signature for Studies in Polyomavirus-Associated Nephropathy. Transplantation. 2016 Oct;100(10):2062-70.

Meyer SE, Qin T, Muench DE, Masuda K, Venkatasubramanian M, Orr E, Suarez L, Gore SD, Delwel R, Paietta E, Tallman MS, Fernandez H, Melnick A, Le Beau MM, Kogan S, Salomonis N, Figueroa ME, Grimes HL. DNMT3A Haploinsufficiency Transforms FLT3ITD Myeloproliferative Disease into a Rapid, Spontaneous, and Fully Penetrant Acute Myeloid Leukemia. Cancer Discov. 2016 May;6(5):501-15.

Grants

Bioinformatics Core for the Progenitor Cell Biology Consortium. Co-Principal Investigator. National Institutes of Health, Heart, Lung and Blood Institute. May 2013-April 2016. U01HL099997.

Identifying Therapeutic Targets for RNA Splicing-Related Cardiomyopathy. Co-Investigator. National Institutes of Health, Heart, Lung and Blood Institute. Dec 2015-Nov 2019. R01 HL130533-01

Mechanisms of Granulocyte Homeostasis. Co-Investigator. National Institutes of Health, Heart, Lung and Blood Institute. July 2015-June 2020. R01 HL122661

A Rapid Spontaneous Murine Model of CN-AML. Co-Investigator. National Cancer Institute. July 2016-June 2019. R01 CA196658.

S. Andrew Spooner, MD, MS, FAAP Chief Medical Information Officer, Biomedical Informatics

S. Andrew Spooner, MD, MS, FAAP, is actively involved in patient-centered research. He is interested in clinical decision support in the pediatric electronic health record.

513-636-2290 andrew.spooner@cchmc.org

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S. Andrew Spooner, MD, MS, FAAP

Chief Medical Information Officer, Biomedical Informatics

Attending Physician, Division of Hospital Medicine

Academic Affiliations

Professor, UC Department of Pediatrics

Phone 513-636-2290

Email andrew.spooner@cchmc.org

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Biography

Dr. Spooner practices general academic pediatrics and serves as the Chief Medical Information Officer for Cincinnati Children’s. He is also actively involved in patient-centered research. He and his research group have created a data warehouse focusing on medication alerts stretching back five years, into which they have built several metrics of user alert-response behavior. They are using this warehouse to answer questions about how clinical users manage the load of decision-support alerts in the system and how they detect potential harmful overdose errors. They are collaborating with an external machine-learning vendor that is working with the hospital’s safety leaders on safety analytics to bring more powerful tools to bear on the problem of alert fatigue and user overload. On other fronts, Dr. Spooner is researching decision support for weight data-entry errors that can have profound effects on medication safety. His group is working with business intelligence systems interfaced to the electronic medical record to tune decision support to unprecedented specificity and sensitivity.

Kirkendall ES, Kouril M, Dexheimer JW, Courter JD, Hagedorn P, Szczesniak R, Li D, Damania R, Minich T, Spooner SA. Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records. J Am Med Inform Assoc. 2016 Aug 9.

Li Q, Spooner SA, Kaiser M, Lingren N, Robbins J, Lingren T, Tang H, Solti I, Ni Y. An end-to-end hybrid algorithm for automated medication discrepancy detection. BMC Med Inform Decis Mak. 2015 May 6;15:37.

Kirkendall ES, Kouril M, Minich T, Spooner SA. Analysis of electronic medication orders with large overdoses: opportunities for mitigating dosing errors. Appl Clin Inform. 2014 Jan 8;5(1):25-45.

Marsolo K, Spooner SA. Clinical genomics in the world of the electronic health record. Genet Med. 2013 Oct;15(10):786-91.

Kirkendall ES, Spooner SA, Logan JR. Evaluating the accuracy of electronic pediatric drug dosing rules. J Am Med Inform Assoc. 2014 Feb;21(e1):e43-9.

Michael Wagner, PhD Faculty Liaison, Biomedical Informatics Core

Michael Wagner, PhD, researches the application of machine learning techniques to bioinformatics problems such as protein structure prediction, disease classification and protein identification. He is also involved in a number of projects that implement complex software and data infrastructure.

513-636-2935 michael.wagner@cchmc.org

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Michael Wagner, PhD

Faculty Liaison, Biomedical Informatics Core

Academic Affiliations

Associate Professor, UC Department of Pediatrics

Phone 513-636-2935

Fax 513-636-2056

Email michael.wagner@cchmc.org

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Clinical

Large-scale optimization; applications in bioinformatics

Research

Large-scale optimization; applications in bioinformatics

Biography

Dr. Wagner has a long-standing interest in applications of machine learning techniques to bioinformatics problems such as protein structure prediction, disease classification and protein identification. He is also involved in a number of projects that implement complex software and data infrastructure. For the National Heart Lung and Blood Institute-funded Pediatric Cardiology Genomics Consortium, part of the Bench to Bassinet project, he plays a leadership role in the development and maintenance of the Data Hub (a.k.a. HeartsMart), which now houses tens of thousands of whole exome and thousands of whole genome sequencing data sets. He is co-principal investigator on the Longitudinal Pediatric Data Resource (LPDR) project funded through the Newborn Screening Translational Research Network and National Institute of Child Health and Human Development. The LPDR is being used by researchers nationwide to mine health outcome data over the lifespan of children who screen positive for rare and often devastating genetic disorders. Dr. Wagner also leads the Rheumatology Disease Research Informatics Core of the Cincinnati Rheumatic Diseases Core Center, which is funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases.

Dipl. Wi-Ing.: Universitaet Karlsruhe, Germany, 1995.

MS: Operations Research, Cornell University, Ithaca, NY, 1998.

PhD: Operations Research, Cornell University, Ithaca, NY, 2000.

Biesiada J, Chidambaran V, Wagner M, Zhang X, Martin LJ, et al. Genetic risk signatures of opioid-induced respiratory depression following pediatric tonsillectomy. Pharmacogenomics. 2014;15(14):1749-1762.

Phatak M, Adamczak R, Cao B, Wagner M, Meller J. Solvent and lipid accessibility prediction as a basis for model quality assessment in soluble and membrane proteins. Current protein & peptide science. 2011;12(6):563-73.

Jain R, Wagner M. Kolmogorov-Smirnov scores and intrinsic mass tolerances for peptide mass fingerprinting. Journal of proteome research. 2010;9(2):737-42.

Wagner M, Adamczak R, Porollo A, Meller J. Linear regression models for solvent accessibility prediction in proteins. Journal of computational biology : a journal of computational molecular cell biology. 2005;12(3):355-69.

Wagner M, Naik DN, Pothen A, Kasukurti S, Devineni RR, Adam BL, Semmes OJ, Wright GL Jr. Computational protein biomarker prediction: a case study for prostate cancer. BMC bioinformatics. 2004;5:26.

Joint Appointment Faculty

Judith W. Dexheimer, PhD

Judith W. Dexheimer, PhD, is a biomedical informatics researcher with an interest in clinical decision support systems with a goal of improving clinical care and patient outcomes. Her research focuses on decision support in the emergency department with an interest in the effectiveness and efficacy of alerts and reminders throughout the hospital.

513-636-7966 judith.dexheimer@cchmc.org

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Judith W. Dexheimer, PhD

Academic Affiliations

Assistant Professor, UC Department of Pediatrics

Phone 513-636-7966

Email judith.dexheimer@cchmc.org

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Medical informatics; clinical decision support; artificial intelligence

Biography

Dr. Dexheimer has a background in developing, implementing and evaluating clinical information systems including clinical decision systems, organizational and workflow aspects of informatics applications, computerized applications for emergency medicine and implementation of artificial intelligence techniques, computerized guideline applications and evidence-based medicine, public health informatics, and preventive care measures. Her research focuses on the design, implementation and evaluation of clinical decision support systems in pediatric emergency medicine to improve clinical care.

Dr. Dexheimer co-leads the Decision Support Analytics Workgroup (DSAW), which investigates the links between the effectiveness of clinical decision support (CDS), patient safety, and user efficiency. This research has demonstrated ties between decreasing alert burden on clinicians, increasing CDS alert salience, and improving patient outcomes.

PhD: Biomedical Informatics, Vanderbilt University, Nashville, TN, 2011.

MS: Biomedical Informatics, Vanderbilt University, Nashville, TN, 2006.

Kirkendall ES, Kouril M, Dexheimer JW, Courter JD, Hagedorn P, Szczesniak R, Li D, Damania R. Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records. J Am Med Inform Assoc. 2016 Aug 9.

Ni Y, Beck AF, Taylor RA, Solti I, Dexheimer JW. Will they participate? Predicting patients’ response to clinical trial invitations in a pediatric emergency department. J Am Med Inform Assoc. 2016 Jul;23(4):671-680.

Ni Y, Kennebeck S, Dexheimer JW, McAneney CM, Tang H, Lingren T, Li Q, Zhai H, Solti I. Automated Clinical Trial Eligibility Pre-Screening: Increasing the Efficiency of Patient Identification for Clinical Trials in the Emergency Department. J Am Med Inform Assoc. 2015 Jan;22(1):166-78.

Dexheimer JW, Abramo TJ, Arnold DH, Johnson KB, Shyr Y, Ye F, Fan K, Patel N, Aronsky D. Implementation and Evaluation of a Computerized Asthma Management System in a Pediatric Emergency Department: A Randomized Clinical Trial. Int J Med Inform. 2014;83(11):805-813.

Dexheimer JW, Sanders DL, Rosenbloom ST, Talbot TR, Aronsky D. Prompting Clinicians: A Systematic Review of Preventive Care Reminders. J Am Med Inform Assoc. 2008 May-Jun;15(3):311-20.

Kevin R. Dufendach, MD, MS Attending Neonatologist, Neonatology and Pulmonary Biology

Kevin R. Dufendach, MD, MS, focuses on user-centered design of electronic health record system software, incorporating human factors principles into the design of human-computer interfaces to better improve information communication for both provider-facing as well as patient-facing applications.

513-636-7018 kevin.dufendach@cchmc.org

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Kevin R. Dufendach, MD, MS

Attending Neonatologist, Neonatology and Pulmonary Biology

Clinical Informaticist, Division of Biomedical Informatics

Academic Affiliations

Assistant Professor, UC Department of Pediatrics

UC Department of Biomedical Informatics

Phone 513-636-7018

Email kevin.dufendach@cchmc.org

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Clinical

Neonatology

Research

Clinical informatics; user-centered design; human-computer interaction; quality improvement; novel software development

Biography

Kevin Dufendach, MD, MS, is an assistant professor in the divisions of Neonatology and Pulmonary Biology and Biomedical Informatics. His research focuses on user-centered design of electronic health record system software. Specifically, he is interested in incorporating human factors principles into the design of human-computer interfaces to better improve information communication for both provider-facing as well as patient-facing applications. Dr. Dufendach’s current research seeks to improve parental engagement in the neonatal intensive care unit through a neonatal-specific inpatient portal application.

BA: Mathematics and Chemistry, Taylor University, Upland, IN, 2006.

MD: Mayo Clinic College of Medicine, Rochester, MN, 2010.

Residency: Pediatrics, Mayo Clinic College of Medicine, Rochester, MN, 2013.

Fellowship: Neonatal and Perinatal Medicine, Vanderbilt University Medical Center, Nashville, TN, 2016.

MS: Biomedical Informatics, Vanderbilt University, Nashville, TN, 2016.

Certification: American Board of Pediatrics, 2013.

Dufendach KR, Lehmann CU. Topics in Neonatal Informatics: Essential Functionalities of the Neonatal Electronic Health Record. NeoReviews. 2015;16(12):e668-73.

Dufendach KR, Eichenberger JA, McPheeters ML, Temple MW, Bhatia HL, Alrifai MW, Potter SA, Weinberg ST, Johnson KB, Lehmann CU. Core Functionality in Pediatric Electronic Health Records. Rockville (MD): Agency for Healthcare Research and Quality (US); 2015 Apr. Report No.: 15-EHC014-EF. AHRQ Comparative Effectiveness Technical Briefs.

Eric S. Hall, PhD

Eric S. Hall, PhD, participates in a number of interdisciplinary teams investigating prematurity and neonatal disease. Along with coordinating data collection and exchange efforts, his work involves the application of knowledge discovery techniques to clinical data sets, as well as the development of software tools to assist in the summarization of clinical data and the modeling of clinical processes.

513-803-2083 eric.hall@cchmc.org

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Eric S. Hall, PhD

Academic Affiliations

Associate Professor, UC Department of Pediatrics

Phone 513-803-2083

Fax 513-803-0968

Email eric.hall@cchmc.org

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Biomedical informatics; clinical informatics; data linkage; infant mortality; maternal child health; neonatal abstinence syndrome; preterm birth; public health informatics

Biography

Dr. Hall leads data management and integration supporting research and quality improvement within the Cincinnati Children's Hospital Medical Center Perinatal Institute. His research expertise includes geospatial and cost analyses, integration of maternal and child data sets enabling population-based research, and the development of risk models to identify perinatal patients at highest risk for adverse outcomes.

PhD: University of Utah, Salt Lake City, UT, 2008.

MS: Brigham Young University, Provo, UT, 2003.

BS: Brigham Young University, Provo, UT, 2001.

Hall ES, Greenberg JM. Estimating community-level costs of preterm birth. Public Health. 2016 Dec;141(12):222-8.

Hall ES, Wexelblatt SL, Greenberg JM. Self-reported and laboratory evaluation of late pregnancy nicotine exposure and drugs of abuse. J Perinatol. 2016 Jul 7.

Hall ES, Meinzen-Derr J, Wexelblatt SL. Cohort analysis of a pharmacokinetic-modeled methadone weaning optimization for neonatal abstinence syndrome. J Pediatr. 2015 Dec;167(6):1221-1225.e1.

Hall ES, Wexelblatt SL, Crowley M, Grow JL, Jasin LR, Klebanoff MA, McClead RE, Meinzen-Derr J, Mohan VK, Stein H, Walsh MC. A multicenter cohort study of treatments and hospital outcomes in neonatal abstinence syndrome. Pediatrics. 2014 Aug;134(2):e527-34.

Hall ES, Goyal NK, Ammerman RT, Miller M, Jones DE, Short JA, Van Ginkel JB. Development of a linked perinatal data resource from state administrative and community-based program data. Matern Child Health J. 2014 Jan;18(1):316-25.

Kenneth M. Kaufman, PhD

Kenneth M. Kaufman, PhD, investigates the genetics of complex and rare disorders using genotyping and next-generation DNA technologies. The goal of his research is to identify the underling mechanisms and genetics that lead to complex diseases such as systemic lupus erythematosus.

513-803-5385 kenneth.kaufman@cchmc.org

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Kenneth M. Kaufman, PhD

Academic Affiliations

Professor, UC Department of Pediatrics

Phone 513-803-5385

Email kenneth.kaufman@cchmc.org

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Genetics; bio-informatics

Biography

A large portion of Dr. Kaufman's research career has been on the genetics of systemic lupus erythematsus. Their work has screened 10's of thousands of lupus cases and controls with millions of polymorphic markers. This work has resulted in the identification, replication and/or fine mapping of over 70 genetic associations with systemic lupus erythematsus.

Recently, they have taken advantage of next generation DNA sequencing to identify variants that directly cause disease. They have developed a number of bioinformatic pipelines that can be applied to any phenotype. These automated pipelines are part of the Cincinnati Analytical Suite for Sequencing Informatics (CASSI) which has been applied to more than 20 different diseases and provides a list of candidate causative variants that lead to disease.

PhD: University of South Carolina, Columbia, SC, 1991.

Kottyan LC, Zoller EE, Bene J, Lu X, Kelly JA, Rupert AM, Lessard CJ, Vaughn SE, Marion M, Weirauch MT, Namjou B, Adler A, Rasmussen A, Glenn S, Montgomery CG, Hirschfield GM, Xie G, Coltescu C, Amos C, Li H, Ice JA, Nath SK, Mariette X, Bowman S; UK Primary Sjögren's Syndrome Registry, Rischmueller M, Lester S, Brun JG, Gøransson LG, Harboe E, Omdal R, Cunninghame-Graham DS, Vyse T, Miceli-Richard C, Brennan MT, Lessard JA, Wahren-Herlenius M, Kvarnström M, Illei GG, Witte T, Jonsson R, Eriksson P, Nordmark G, Ng WF; UK Primary Sjögren's Syndrome Registry, Anaya JM, Rhodus NL, Segal BM, Merrill JT, James JA, Guthridge JM, Scofield RH, Alarcon-Riquelme M, Bae SC, Boackle SA, Criswell LA, Gilkeson G, Kamen DL, Jacob CO, Kimberly R, Brown E, Edberg J, Alarcón GS, Reveille JD, Vilá LM, Petri M, Ramsey-Goldman R, Freedman BI, Niewold T, Stevens AM, Tsao BP, Ying J, Mayes MD, Gorlova OY, Wakeland W, Radstake T, Martin E, Martin J, Siminovitch K, Moser Sivils KL, Gaffney PM, Langefeld CD, Harley JB, Kaufman KM. The IRF5-TNPO3 association with systemic lupus erythematosus has two components that other autoimmune disorders variably share. Hum Mol Genet. 2015 Jan 15;24(2):582-96.

Kaufman KM, Linghu B, Szustakowski JD, Husami A, Yang F, Zhang K, Filipovich AH, Fall N, Harley JB, Nirmala NR, Grom AA. Whole-exome sequencing reveals overlap between macrophage activation syndrome in systemic juvenile idiopathic arthritis and familial hemophagocytic lymphohistiocytosis. Arthritis Rheumatol. 2014 Dec;66(12):3486-95.

Patel ZH, Kottyan LC, Lazaro S, Williams MS, Ledbetter DH, Tromp H, Rupert A, Kohram M, Wagner M, Husami A, Qian Y, Valencia CA, Zhang K, Hostetter MK, Harley JB, Kaufman KM. The struggle to find reliable results in exome sequencing data: filtering out Mendelian errors. Front Genet. 2014 Feb 12;5:16.

Kaufman KM, Zhao J, Kelly JA, Hughes T, Adler A, Sanchez E, Ojwang JO, Langefeld CD, Ziegler JT, Williams AH, Comeau ME, Marion MC, Glenn SB, Cantor RM, Grossman JM, Hahn BH, Song YW, Yu CY, James JA, Guthridge JM, Brown EE, Alarcón GS, Kimberly RP, Edberg JC, Ramsey-Goldman R, Petri MA, Reveille JD, Vilá LM, Anaya JM, Boackle SA, Stevens AM, Freedman BI, Criswell LA, Pons Estel BA; Argentine Collaborative Group, Lee JH, Lee JS, Chang DM, Scofield RH, Gilkeson GS, Merrill JT, Niewold TB, Vyse TJ, Bae SC, Alarcón-Riquelme ME; BIOLUPUS network, Jacob CO, Moser Sivils K, Gaffney PM, Harley JB, Sawalha AH, Tsao BP. Fine mapping of Xq28: both MECP2 and IRAK1 contribute to risk for systemic lupus erythematosus in multiple ancestral groups. Ann Rheum Dis. 2013 Mar;72(3):437-44.

International Consortium for Systemic Lupus Erythematosus Genetics (SLEGEN), Harley JB, Alarcón-Riquelme ME, Criswell LA, Jacob CO, Kimberly RP, Moser KL, Tsao BP, Vyse TJ, Langefeld CD, Nath SK, Guthridge JM, Cobb BL, Mirel DB, Marion MC, Williams AH, Divers J, Wang W, Frank SG, Namjou B, Gabriel SB, Lee AT, Gregersen PK, Behrens TW, Taylor KE, Fernando M, Zidovetzki R, Gaffney PM, Edberg JC, Rioux JD, Ojwang JO, James JA, Merrill JT, Gilkeson GS, Seldin MF, Yin H, Baechler EC, Li QZ, Wakeland EK, Bruner GR, Kaufman KM, Kelly JA. Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. Nat Genet. 2008 Feb;40(2):204-10.

Eric S. Kirkendall, MD, MBI, FAAP Associate Chief Medical Information Officer, Information Services

Eric S. Kirkendall, MD, MBI, FAAP, performs research in patient safety, quality improvement, and resource utilization. He is particularly interested in the design and development of novel software and applications to address complex healthcare system challenges, employing techniques such as real-time data acquisition, advanced analytics, artificial intelligence, human factors, and user-centered design.

513-636-1945 eric.kirkendall@cchmc.org

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Eric S. Kirkendall, MD, MBI, FAAP

Associate Chief Medical Information Officer, Information Services

Attending Hospitalist, Division of Hospital Medicine

Clinical Informaticist, Division of Biomedical Informatics

Academic Affiliations

Associate Professor, UC Department of Pediatrics

UC Department of Biomedical Informatics

Phone 513-636-1945

Fax 513-803-9245

Email eric.kirkendall@cchmc.org

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Clinical

Hospital medicine

Research

Medical/clinical informatics; patient safety; quality improvement; artificial intelligence; novel software development

Biography

Dr. Kirkendall works to use health information technology to maximize patient safety and quality in clinical care delivery, data management, and novel application/software development. He is the first Associate Chief Medical Information Officer in Cincinnati Children's history. In that role he oversees the design, implementation, and optimization of the electronic health record and other associated technologies.

Dr. Kirkendall co-leads the Decision Support Analytics Workgroup (DSAW), which investigates the links between the effectiveness of clinical decision support (CDS), patient safety, and user efficiency. His research has demonstrated ties between decreasing alert burden on clinicians, increasing CDS alert salience, and improving patient outcomes.

Many of Dr. Kirkendall’s research projects have also incorporated artificial intelligence techniques (e.g., natural language processing) and other innovative methods to detect adverse events/harm across multiple hospital environments. The results have shown vast improvements in detecting errors related to medication administrations.

Dr. Kirkendall has also worked with the Center for Acute Care Nephrology to develop a catalog of detection and risk-stratifying electronic triggers that have resulted in NTMx-AKI reductions of 25-50% across four novel metrics, preventing over 400 children from developing AKI.

BS: Biology, University of Toledo, Toledo, OH, 1999.

MD: University of Cincinnati, Cincinnati, OH, 2003.

Residency: Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 2006.

MBI: Oregon Health & Science University, Portland, OR, 2012.

Certification: American Board of Pediatrics, 2006; Clinical Informatics, 2014.

Goldstein SL, Mottes T, Simpson K, Barclay C, Muething S, Haslam DB, Kirkendall ES. A sustained quality improvement program reduces nephrotoxic medication-associated acute kidney injury. Kidney Int. 2016 Jul;90(1):212-21.

Lehmann CU, Council on Clinical Information Technology, Weinberg ST, Alexander GM, Beyer EL, Del Beccaro MA, Francis AB, Handler EG, Johnson TD, Kirkendall ES, Lighter DE, Morgan SJ, Raskas MD, Tham E, Webber EC. Pediatric Aspects of Inpatient health Information Technology Systems. Pediatrics. 2015 Mar.

Goldstein SL, Kirkendall E, Nguyen H, Schaffzin JK, Bucuvalas J, Bracke T, Seid M, Ashby M, Foertmeyer N, Brunner L, Lesko A, Barclay C, Lannon C, Muething S. Electronic Health Record Identification of Nephrotoxin Exposure and Associated Acute Kidney Injury. Pediatrics. 2013 Sep;132(3):e756-67.

Kirkendall ES, Kloppenborg E, Papp J, White D, Frese C, Hacker D, Schoettker PJ, Muething S, Kotagal U. Measuring Adverse Events and Levels of Harm in Pediatric Inpatients with the Global Trigger Tool. Pediatrics. 2012 Nov;130(5):e1206-14.

Narus SP, Srivastava R, Gouripeddi R, Livne OE, Mo P, Bickel JP, de Regt D, Hales JW, Kirkendall E, Stepanek RL, Toth J, Keren R. Federating clinical data from six pediatric hospitals: process and initial results from the PHIS+ Consortium. AMIA Annu Symp Proc. 2011;2011:994-1003.

Grants

Improving Intensive Care Medication Safety through EHR-based Algorithms. Principal Investigator. National Library of Medicine. 2015-2019.

NINJA2: Reduction of Nephrotoxic Medication-associated Acute Kidney Injury in Children. Co-investigator. Agency for Healthcare Research and Quality. 2015-2018.

Pursuing Perfection in Pediatric Therapeutics. Clinical Scientist – Informatics Expert. Agency for Healthcare Research and Quality. 2011-2016.

Kakajan Komurov , PhD Member, Division of Experimental Hematology & Cancer Biology

Dr. Komurov's lab integrates computational and experimental approaches to characterize targetable cancer vulnerabilities. Their driver projects include exploiting oncogene-induced stress pathways in cancer therapy, and the study of mechanisms, implications and therapeutic vulnerabilities imposed by the severe epigenetic defects in a subset of cancers.
Visit the Komurov Lab.

513-803-5122 kakajan.komurov@cchmc.org

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Kakajan Komurov , PhD

Member, Division of Experimental Hematology & Cancer Biology

Academic Affiliations

Assistant Professor, UC Department of Pediatrics

Phone 513-803-5122

Email kakajan.komurov@cchmc.org

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Computational and experimental approaches to characterize targetable cancer vulnerabilities; oncogene-induced stress pathways in cancer therapy; the study of mechanisms, implications and therapeutic vulnerabilities imposed by the severe epigenetic defects in a subset of cancers

Visit the Komurov Lab.

Biography

Dr. Komurov's lab focuses on the systems biology of cancer. We develop and employ computational data mining tools to interrogate clinically exploitable cancer mechanisms from cancer genomics data, and use experimental approaches in vitro and in animal models for their molecular characterization. Specifically, we are studying the core aberrations in the genomic, RNA and protein homeostasis networks in cancers, their role in cancer pathogenicity and therapy response, and the synthetic vulnerabilities imposed by these defects on the tumor cell. In addition, we are developing computational methods and software to enable intuitive and effective functional mining of genomic data.

PhD: The University of Texas Southwestern Medical Center, Dallas, TX.

Grants

Modeling and targeting the hexosamine pathway in drug resistance. Principal Investigator. Susan G. Komen for the Cure. Aug 2013-Jul 2016. CCR13263034.

Exploiting proteotoxic stress in therapy-refractory HER2+ breast cancers. Principal Investigator. National Cancer Institute. Apr 2015-Mar 2020. 1R01CA193549-01.

Global Transcript Shortening in cancers. Principal Investigator. Cincinnati Children's Hospital Medical Center RIP award. Sep 2015-Aug 2016.

Mario Medvedovic, PhD

Dr. Medvedovic is developing and applying new statistical and computational methods for the analysis of "big data" in the context of biomedical research. His recent work is focused on the reconstruction of regulatory networks using libraries of genome-scale signatures of cellular perturbations. He is also developing protocols for analyzing next-generation sequencing data, and working on development and application of unsupervised statistical learning approaches based on the non-parametric Bayesian models. He is also the director of the Division of Biostatistics and Bioinformatics in UC's Department of Environment Health.
Visit the Medvedovic Lab.

513-558-8564 mario.medvedovic@uc.edu

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Mario Medvedovic, PhD

Academic Affiliations

Professor, UC Department of Environmental Health

Phone 513-558-8564

Email mario.medvedovic@uc.edu

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Biography

Dr. Medvedovic is developing and applying new statistical and computational methods for the analysis of “big data” in the context of biomedical research. His recent work is focused on the reconstruction of regulatory networks using libraries of genome-scale signatures of cellular perturbations. He is also developing protocols for analyzing next-generation sequencing data, and working on development and application of unsupervised statistical learning approaches based on the non-parametric Bayesian models. He is also the director of the Division of Biostatistics and Bioinformatics in UC's Department of Environment Health.

Master’s: University of Zagreb, Zagreb, Croatia 1992.

Master’s: Bowling Green State University, Bowling Green, OH, 1994.

PhD: University of Cincinnati, Cincinnati, OH, 1998.

View Google Scholar Publications

Chen J, Hu Z, Phatak M, Reichard J, Freudenberg JM, Sivaganesan S, Medvedovic M. Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules. PLoS Comp Biol. 2013;9:e1003198.

Ma L, Tao Y, Duran A, Llado V, Galvez A, Barger JF, Castilla EA, Chen J, Yajima T, Porollo A, Medvedovic M, Brill LM, Plas DR, Riedl SJ, Leitges M, Diaz-Meco MT, Richardson AD, Moscat J. Control of nutrient stress-induced metabolic reprogramming by PKCzeta in tumorigenesis. Cell. 2013;152(3):599-611.

Shinde K, Phatak M, Freudenberg JM, Chen J, Li Q, Joshi VK, Hu Z, Ghosh K, Meller J, Medvedovic M.* Genomics Portals: Integrative Web-Platform for Mining Genomics Data. BMC Genomics. 2010 Jan 13;11(1):27.

Medvedovic M, Sivaganesan S. Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics. 2002;18:1194-1206.

Nikiforova M, Stringer JR, Blough R, Medvedovic M, Fagin JA, Nikiforov YA. Proximity of Chromosomal Loci That Participate in Radiation-Induced Translocations in Human Cells. Science. 2000;290:138-141.

Jarek Meller, PhD Graduate Program Director, Division of Biomedical Informatics

Dr. Meller researches the development and application of computational methods for learning from biological data, including applications to protein structure prediction, mapping protein interactions, functional annotation and genotype-phenotype associations. State-of-the-art modeling and annotation tools developed by his group are publicly available.
Visit the Meller Lab.

513-636-0270 jmeller@cchmc.org

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Jarek Meller, PhD

Graduate Program Director, Division of Biomedical Informatics

Academic Affiliations

Associate Professor, UC Department of Environmental Health

Phone 513-636-0270

Fax 513-636-2056

Email jmeller@cchmc.org

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Visit the Meller Lab.

Biography

Dr. Meller serves as graduate program director for Biomedical Informatics and also pursues several lines of research in molecular modeling, structural bioinformatics and computational genomics, at the intersection of data science and biomedicine.

Dr. Meller and his group have developed a number of successful methods for the prediction of protein structure, protein-protein interactions and functional hot spots in proteins. Several web servers developed by the group, including Sable, Sppider, Minnou and Polyview have widely been used, with a total of over 1 million submissions from more than 30,000 users in many countries.

Dr. Meller has also been active in the development and applications of methods for knowledge extraction from high dimensional genomic data. He and his group have been involved in many collaborative projects with direct medical relevance. Examples include identification of markers associated with disease subtypes in cancer and autoimmunity, modeling of signal transduction pathways in differentiation and development, and developing inhibitors of critical protein-protein interactions in autophagy, bone marrow transplants, and pathogen-host interactions.

PhD: Biomedical Engineering, Polish Academy of Sciences, Warsaw, 2004.

Porollo A, Meller J. Prediction-based fingerprints of protein-protein interactions. Proteins. 2007 Feb 15;66(3):630-45.

Sinha AU, Meller J. Cinteny: flexible analysis and visualization of synteny and genome rearrangements in multiple organisms. BMC Bioinformatics. 2007 Mar 8;8:82.

Cao B, Porollo A, Adamczak R, Jarrell M, Meller J. Enhanced recognition of protein transmembrane domains with prediction-based structural profiles. Bioinformatics. 2006 Feb 1;22(3):303-9.

Adamczak R, Porollo A, Meller J. Combining prediction of secondary structure and solvent accessibility in proteins. Proteins. 2005 May 15;59(3):467-75.

Adamczak R, Porollo A, Meller J. Accurate prediction of solvent accessibility using neural networks-based regression. Proteins. 2004 Sep 1;56(4):753-67.

Emily R. Miraldi, PhD

Dr. Miraldi is a computational and systems biologist who builds mathematical models of the immune system from high-dimensional genomics measurements. Her studies leverage biotechnologies, including chromatin accessibility and single-cell gene expression measurements, and require new computational methods development. Miraldi’s long-term goal is to use these models to reengineer immune-cell behavior in the context of autoimmune and other diseases.

513-517-2063 emily.miraldi@cchmc.org

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Emily R. Miraldi, PhD

Academic Affiliations

Assistant Professor, UC Department of Pediatrics

Phone 513-517-2063

Email emily.miraldi@cchmc.org

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Systems biology; computational immunology; immuno-engineering; network inference

Biography

Our research goal is immuno-engineering: to alter the behavior of specific immune cell populations in disease contexts (autoimmune disease, organ transplant, and cancer) without compromising the body’s homeostatic immune function (e.g. defense against pathogens). Thus, it is critical to develop a nuanced understanding of how different immune cells sense and respond to environmental cues across the body, in both physiological and disease settings. To this end, our lab’s major focus is reverse-engineering the underlying logic of immune cells (molecular networks that drive cellular responses) from high-dimensional molecular measurements of immune cells in action (sensing and responding to perturbations, disease conditions, etc.).

The lab’s focus is transcriptional regulatory network inference, modeling gene expression as a function of transcription factor activities, from gene expression and measurements of chromatin state. Chromatin accessibility measurements by ATAC-seq, together with transcription-factor DNA-binding preferences (motifs), can be used to broadly profile potential transcription factor binding events in relatively small populations of cells. Thus, we have used ATAC-seq with RNA-seq to enable de novo inference of transcriptional regulatory networks in physiological settings where sample material is limiting (e.g., intestinal immune cells in response to microbial/genetic perturbations). To date, most of these efforts have been in mouse models, as it is generally not possible to obtain sufficient sample material for similar experimental designs in human. To build human immune cell models, we are developing multi-task learning approaches to leverage evolutionarily conserved relationships and borrow statistical power from mouse datasets for inference in human. We are also developing methods to enable transcriptional regulatory network inference in very rare cell populations, as measured from single-cell RNA-seq experiments.

BA: Oberlin College, Oberlin, OH.

PhD: Massachusetts Institute of Technology, Cambridge, MA.

Postdoctoral Fellow: New York University, New York School of Medicine, Flatiron Institute (Simons Foundation), New York, NY.

Karwacz K, Miraldi ER, Pokrovskii M, Madi A, Yosef N, Wortman I, Chen X, Watters A, Carriero N, Awasthi A, Regev A, Bonneau R, Littman DR, Kuchroo VK. Critical role of IRF1 and BATF in preparing the chromatin landscape during Tr1 differentiation. Nature Immunology. 2017.

Rocha PP, Raviram R, Fu Y, Kim J, Luo V, Aljoufi A, Swanzey E, Pasquarella A, Balestrini A, Miraldi ER, Bonneau R, Petrini J, Schotta G, Skok JA. A damage-independent role for 53BP1 that impacts break order and Igh architecture during class switch recombination. Cell Rep. 2016 Jun 28;16(1):48-55.

Raviram R, Rocha PD, Mueller CL, Miraldi ER, Fu Y, Swanzey E, Badri S, Proudhon C, Snetkova V, Bonneau R, Skok JA. 4C-ker: A method to reproducibly identify genome-wide interactions captured by 4C-Seq experiments. PLoS Comput Biol. 2016 Mar 3;12(3):e1004780.

Huang W, Thomas B, Flynn RA, Gavzy SJ, Wu L, Kim SV, Hall JA, Miraldi ER, Ng CP, Lee B, Rigo FW, Meadows S, Montoya NR, Herrera N, Domingos AI, Rastinejad F, Myers RM, Fuller-Pace FV, Bonneau R, Chang HY, Acuto O, Littman DR. DDX5 and its associated lncRNA Rmrp modulate Th17 cell effector functions. Nature. 2015 Dec 24;528(7583):517-22.

Sano T, Huang W, Hall JA, Chen A, Gravzy SJ, Lee J, Ziel J, Miraldi ER, Domingos AJ, Bonneau R, Littman DR. An IL-23R/IL-22 circuit regulates epithelial serum amyloid A to promote local effector Th17 responses. Cell. 2015;163(2):381-393.

Kurtz ZD, Mueller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau R. Sparse and compositionally robust inference of microbial ecological networks. PLoS Computational Biology. 2015;11(5):e1004226.

Yu B, Doraiswamy H, Chen X, Miraldi ER, Ortiz M, Hafemeister C, Bonneau R, Silva CT. Genotet: an interactive web-based visual exploration framework to support validation of gene regulatory networks. IEEE Trans Vis Comput Graph. 2014 Dec;20(12):1903-12.

Longman RS, Diehl GE, Victorio DA, Huh JH, Galan C, Miraldi ER, Swaminath A, Bonneau R, Scherl EJ, Littman DR. CX3CR1+ mononuclear phagocytes support colitis-associated innate lymphoid cell production of IL-22. Journal of Experimental Medicine. 2014;211(8):1571-1583.

Miraldi ER, Sharfi H, Friedline RH, Johnson H, Zhang T, Lau K, Ko HJ, Curran TG, Haigis KM, Yaffe YB, Bonneau R, Lauffenburger DA, Kahn BB, Kim J, Neel BG, Saghatelian A, White FM. Molecular network analysis of protein-tyrosine phosphorylation and altered lipid metabolism in liver-specific PTP1b deletion mice. Integrative Biology. 2013;5(7):940-963.

Huang PH, Miraldi ER, Xu AM, Kundukulam VA, Del Rosario AM, Flynn RA, Cavenee WK, Furnari FB, White FM. Phosphotyrosine signaling analysis of site-specific mutations on EGFRvIII identifies determinants governing glioblastoma cell growth. Molecular BioSystems. 2010;6(7):1227-37.

Miraldi ER, Thomas PJ, Romberg L. Allosteric models for cooperative polymerization of linear polymers. Biophysical Journal. 2008;95(5):2470-2486.

Alexey Porollo, PhD Member, Center for Autoimmune Genomics and Etiology

Dr. Porollo is a computational biologist with research focused on the development of new prediction and analytical methods in structural bioinformatics.
Visit the Porollo Lab website.

513-803-5489 alexey.porollo@cchmc.org

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Alexey Porollo, PhD

Member, Center for Autoimmune Genomics and Etiology

Member, Division of Biomedical Informatics

Academic Affiliations

Assistant Professor, UC Department of Pediatrics

UC Department of Environmental Health

Phone 513-803-5489

Email alexey.porollo@cchmc.org

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Computational biology; bioinformatics
Visit the Porollo Lab website.

Biography

Dr. Porollo is a computational biologist with research focused on the development of new prediction and analytical methods in structural bioinformatics. Applications of computational approaches include structural and functional characterization of proteins and their mutations, rational protein engineering, analysis of biological pathways, identification of new drug targets, virtual drug screening, and microbiome analysis.

MSc: Mari State University, Yoshkar-Ola, Russia, 1995.

PhD: Tver State University, Tver and Mari State University, Yoshkar-Ola, Russia, 1999.

Post-doc: Children’s Hospital Medical Center, Cincinnati, OH, 2006.

Baker FN, Porollo A. CoeViz: a web-based tool for coevolution analysis of protein residues. BMC Bioinformatics. 2016 Mar 8;17:119.

Porollo A, Sesterhenn TM, Collins MS, Welge JA, Cushion MT. Comparative Genomics of Pneumocystis Species Suggests the Absence of Genes for myo-Inositol Synthesis and Reliance on Inositol Transport and Metabolism. MBio. 2014 Nov 4;5(6).

Syed K, Porollo A, Miller D, Yadav JS. Rational engineering of the fungal P450 monooxygenase CYP5136A3 to improve its oxidizing activity toward polycyclic aromatic hydrocarbons. Protein Eng Des Sel. 2013 Sep;26(9):553-7.

Porollo A, Meller J. POLYVIEW-MM: web-based platform for animation and analysis of molecular simulations. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W662-6.

Porollo A, Meller J. Prediction-based fingerprints of protein-protein interactions. Proteins. 2007 Feb 15;66(3):630-45.

Grants

Directed Culturing of Pneumocystis Using Metatranscriptomics. Co-Principal Investigator. National Institutes of Health: Heart, Lung and Blood Institute. May 2013-Feb 2018. R01HL119190-01.

GM-CSF-Induced Metal Sequestration and Histoplasma. Co-Investigator. National Institute of Allergy and Infectious Diseases. May 2013–Apr 2018. 1R01AI106269-01.

Suppression of IgE-Mediated Disease by Polyclonal Rapid Desensitization. Co-Investigator. National Institute of Allergy and Infectious Diseases. Jul 2014-Jun 2019. 1R01AI113162-01.

Alexander J. Towbin, MD Radiologist, Department of Radiology and Medical Imaging

Dr. Towbin is interested in radiology informatics; cancer imaging and abdominal imaging.

513-636-5896 alexander.towbin@cchmc.org

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Alexander J. Towbin, MD

Radiologist, Department of Radiology and Medical Imaging

Neil D. Johnson Chair, Radiology Informatics

Associate Chief, Radiology Informatics

Co-Chief, Thoracoabdominal Imaging

Academic Affiliations

Associate Professor, UC Department of Radiology

Phone 513-636-5896

Fax 513-803-3015

Email alexander.towbin@cchmc.org

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Clinical

Radiology informatics; cancer imaging; abdominal imaging

Research

Radiology informatics; cancer imaging; abdominal imaging

Biography

Dr. Towbin is a radiologist, the Neil D. Johnson Chair of Radiology Informatics, and associate chief of Radiology, Clinical Operations and Radiology Informatics at Cincinnati Children's Hospital Medical Center. He is a recognized leader in enterprise imaging, structured reporting, and workflow efficiency. In his clinical role, Dr. Towbin specializes in abdominal imaging. His research focuses on radiology clinical informatics, quality improvement, cancer imaging, and imaging of the liver.

MD: Doctor of Medicine, University of Cincinnati, Cincinnati, OH, 2002.

Internship: Pediatrics, Cincinnati Children’s Hospital Medical Center-University of Cincinnati College of Medicine, Cincinnati, OH, 2002-2003.

Residency: Diagnostic Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, 2003-2007.

Fellowship: Pediatric Radiology, Cincinnati Children’s Hospital Medical Center-University of Cincinnati College of Medicine, Cincinnati, OH, 2008.

Certification: American Board of Radiology, 2007.

Larson DB, Trout AT, Fierke SR, Towbin AJ. Improvement in diagnostic accuracy of ultrasound of the pediatric appendix through the use of equivocal interpretive categories. AJR: American journal of roentgenology. 2015;204(4):849-56.

Larson DB, Towbin AJ, Pryor RM, Donnelly LF. Improving consistency in radiology reporting through the use of department-wide standardized structured reporting. Radiology. 2013 Apr;267(1):240-50.

Towbin AJ, Iyer SB, Brown J, Varadarajan K, Perry LA, Larson DB. Practice policy and quality initiatives: decreasing variability in turnaround time for radiographic studies from the emergency department. Radiographics. 2013 Mar-Apr;33(2):361-71.

Smith EA, Salisbury S, Martin R, Towbin AJ. Incidence and etiology of new liver lesions in pediatric patients previously treated for malignancy. AJR Am J Roentgenol. 2012 Jul;199(1):186-91.

Towbin AJ, Paterson B, Chang PJ. A computer-based radiology simulator as a learning tool to help prepare first-year residents for being on call. Acad Radiol. 2007 Oct;14(10):1271-83.

Matthew T. Weirauch, PhD

Matthew T. Weirauch, PhD, is a computational biologist. His lab seeks to understand the mechanisms of gene transcriptional regulation. Current projects focus on characterizing transcription factor binding specificities, and developing methods for modeling their interactions with DNA, both in vitro and in vivo. His lab applies insights from basic research on transcription factor-DNA interactions to study the mechanisms underlying complex diseases.

Visit the Weirauch Lab.

513-803-9078 matthew.weirauch@cchmc.org

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Matthew T. Weirauch, PhD

Academic Affiliations

Assistant Professor, UC Department of Pediatrics

Phone 513-803-9078

Email matthew.weirauch@cchmc.org

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Transcription factors; transcriptional regulation; functional genomics; genome analysis

Visit the Weirauch Lab.

Biography

Dr. Weirauch is a computational biologist. His lab seeks to understand the mechanisms of gene transcriptional regulation. Current projects focus on characterizing transcription factor binding specificities, and developing methods for modeling their interactions with DNA, both in vitro and in vivo. His lab applies insights from basic research on transcription factor-DNA interactions to study the mechanisms underlying complex diseases.

Postdoctoral Fellow: University of Toronto (Donnelly Center for Cellular and Biomolecular Research), Toronto, Ontario, Canada.

PhD: Bioinformatics, University of California Santa Cruz, Santa Cruz, California.

BSc: Computer Science, Pennsylvania State University, University Park, PA.

Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015 Aug;33(8):831-8.

Narasimhan K, Lambert SA, Yang AW, Riddell J, Mnaimneh S, Zheng H, Albu M, Najafabadi HS, Reece-Hoyes JS, Fuxman Bass JI, Walhout AJ, Weirauch MT, Hughes TR. Mapping and analysis of Caenorhabditis elegans transcription factor sequence specificities. Elife. 2015 Apr 23;4.

Weirauch M, Yang A, Albu M, Cote A, Montenegro-Montero A, Drewe P, Najafabadi H, Lambert S, Mann I, Cook K, Zheng H, Goity A, van Bakel H, Lozano J, Galli M, Lewsey M, Huang E, Mukherjee T, Chen X, Reece-Hoyes J, Govindarajan S, Shaulsky G, Walhout AJM, Bouget F, Ratsch G, Larrondo L, Ecker J, Hughes T. Determination and inference of eukaryotic transcription factor sequence specificity. Cell. 2014 Sep 11;158(6):1431-1443.

Ray D*, Kazan H*, Cook K*, Weirauch M*, Najafabadi H*, Li X, Gueroussov S, Albu M, Zheng H, Yang A, Na H, Irimia M, Matzat L, Dale R, Smith S, Yarosh C, Kelly S, Nabet B, Mecenas D, Li W, Laishram R, Qiao M, Lipshitz H, Piano F, Corbett A, Carstens R, Frey B, Anderson R, Lynch K, Penalva L, Lei E, Fraser A, Blencowe B, Morris Q, Hughes T. A compendium of RNA binding motifs for decoding gene regulation. Nature. 2013 Jul 10;499(7457):172-177. *co-first authors.

Weirauch M, Cote A, Norel R, Annala M, Zhao Y, Riley T, Saez-Rodriguez J, Cokelaer T, Vedenko A, Talukder S, DREAM5 Consortium, et al. Evaluation of methods for modeling transcription factor sequence specificity. Nature Biotechnology. 2013 Jan 27;31(2):126-34.

Grants

Viral transcription factor interactions with disease-associated genetic variants. Principal Investigator. Trustee Award, Cincinnati Children's. Jul 2014-Jun 2016.

Translational Genomics Analysis Core. Co-Investigator. NIH/NCRR CCTST T1 Pilot, Cincinnati Children's. Jul 2014-Jun 2016.

Decoding C2H2 Zinc Fingers. Collaborator. Canadian Institutes of Health Research (CIHR) Operating Grant. Oct 2013-Sep 2016.

Danny T. Y. Wu, PhD

Dr. Wu's research draws on human-computer interaction, data mining, information retrieval, and natural language processing to maximize the value of clinical data stored in electronic health records to improve care quality and support clinical and translational research.

513-558-6464 wutz@ucmail.uc.edu

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Danny T. Y. Wu, PhD

Academic Affiliation

Assistant Professor

UC Department of Biomedical Informatics

Phone 513-558-6464

Email wutz@ucmail.uc.edu

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Biography

Danny T.Y. Wu recently joined the University of Cincinnati Department of Biomedical Informatics (BMI) as an assistant professor. His research draws on human-computer interaction, data mining, information retrieval, and natural language processing to maximize the value of clinical data stored in electronic health records to improve care quality and support clinical and translational research. Wu received both his PhD and master’s degree from the University of Michigan School of Information prior to joining UC. Before going to graduate school, he worked as a software engineer for four years. In addition to research, Wu is dedicated to education, service, and practical engagement. He was appointed as a lecturer in the Department of Health Management and Policy at the University of Michigan in Fall 2015, teaching a graduate-level course on database systems and Internet applications. He was a senior analyst leading a programming team to develop, implement, and innovate dynamic data capture systems at the University of Michigan Congenital Heart Center. Wu served on the student editorial board of the Journal of American Medical Informatics Association in 2015 and 2016.

PhD: University of Michigan School of Information, Ann Arbor, MI.

MS: University of Michigan School of Information, Ann Arbor, MI.

BBA: National Taiwan University, Taipei City, Taiwan.

Wu DT, Hanauer DA, Mei Q, Clark PM, An LC, Proulx J, Zeng QT, Vydiswaran VG, Collins-Thompson K, Zheng K. Assessing the readability of ClinicalTrials.gov. J Am Med Inform Assoc. 2016 Mar;23(2):269-75.

Wu DT, Hanauer DA, Mei Q, Clark PM, An LC, Lei J, Proulx J, Zeng-Treitler Q, Zheng K. Applying multiple methods to assess the readability of a large corpus of medical documents. Stud Health Technol Inform. 2013;192:647-51.

Bradley DJ, Wu DT, Goldberg CS, Serwer GS, Lowery RE, Donohue JE, Hirsch-Romano JC, Zheng K, Pasquali SK. Out of many, one: integrating data in the paediatric cardiovascular environment. Cardiol Young. 2016 Sep 29:1-7.

Yan Xu, PhD Director of Bioinformatics Core, Neonatology & Pulmonary Biology; Perinatal Institute

Dr. Xu's research interests include the identification of gene signatures, regulatory networks and biological pathways controlling lung development, maturation and diseases. She is actively involved in the development of detailed developmental lung-map via high-throughput single cell genomics to provide useful tools and resources for the lung research community.

513-636-8921 yan.xu@cchmc.org

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Yan Xu, PhD

Director of Bioinformatics Core, Neonatology & Pulmonary Biology; Perinatal Institute

Academic Affiliations

Professor, UC Department of Pediatrics

UC Department of Biomedical Informatics

Phone 513-636-8921

Fax 513-636-7868

Email yan.xu@cchmc.org

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Bioinformatics; systems biology application in pulmonary research; lung cell type specific signaling; asthma associated pathology

Biography

Dr. Xu’s research interest is to develop and apply bioinformatics and systems biology approaches to gain a better understanding of molecular mechanisms behind big data sets. Her current lines of research are focusing on the identification of gene signatures, regulatory networks, and biological pathways controlling lung maturation and diseases.

She is actively involved in using high-throughput single cell genomics for the development of LungMAP, a web-based data resource funded by the National Heart, Lung and Blood Institute to provide useful tools and resources for the lung research community.

BS: Pharmacology, Shanghai Medical University, China, 1986.

MS: Pathology, Shanghai Medical University, China, 1989.

PhD: Molecular and Cellular Pharmacology, University of South Alabama, Mobile, Alabama, 1997.

Kamath-Rayne BD, Du Y, Hughes M, Wagner EA, Muglia LJ, DeFranco EA, Whitsett JA, Salomonis N, Xu Y. Systems biology evaluation of cell-free amniotic fluid transcriptome of term and preterm infants to detect fetal maturity. BMC Med Genomics. 2015;8(1):67.

Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis. PLoS Comput Biol. 2015;11(11):e1004575.

Du Y, Guo M, Whitsett JA, Xu Y. 'LungGENS': a web-based tool for mapping single-cell gene expression in the developing lung. Thorax. 2015 Jun 30.

Xu Y, Wang Y, Besnard V, Ikegami M, Wert SE, Heffner C, Murray SA, Donahue LR, Whitsett JA. Transcriptional programs controlling perinatal lung maturation. PLoS One. 2012;7(8):e37046.

Xu Y, Zhang M, Wang Y, Kadambi P, Dave V, Lu LJ, Whitsett JA. A systems approach to mapping transcriptional networks controlling surfactant homeostasis. BMC Genomics. 2010;11(1):451.

Grants