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Peter S. White, PhD Director, Division of Biomedical Informatics
serves as the director of the Division of Biomedical Informatics at Cincinnati Children’s Hospital Medical Center and as the Rieveschl Chair of the Department of Biomedical Informatics at the University of Cincinnati (UC) College of Medicine. He oversees informatics research and resources at both institutions. He also serves as co-director of Cincinnati Children’s Center for Pediatric Genomics.
Director, Division of Biomedical Informatics
Professor, UC Department of Biomedical Informatics
Bioinformatics; biomedical informatics; genome informatics
Peter S. White, PhD, serves as the director of the Division of Biomedical Informatics at Cincinnati Children’s Hospital Medical Center and as the Rieveschl Chair of the Department of Biomedical Informatics at the University of Cincinnati (UC) College of Medicine. He oversees informatics research and resources at both institutions, including academic, research, and collaborative service missions. He also serves as co-director of Cincinnati Children’s Center for Pediatric Genomics.
In his research career, White has explored the development and application of novel approaches for disease gene discovery. He has developed innovative approaches for integrating and disseminating clinical, phenotypic, and molecular data to researchers for promoting discovery and hypothesis validation. He is currently playing a lead informatics role on a number of national data projects. White came to Cincinnati Children’s from the Children’s Hospital of Philadelphia, where he launched the Research Institute’s Center for Biomedical Informatics in 2006 and oversaw that center’s expansion in genome analysis. He has an undergraduate degree in biology and received a PhD in Molecular Genetics at Washington University in St. Louis in 1992.
Li MH, Abrudan JL, Dulik MC, Sasson A, Brunton J, Jayaraman V, Dugan N, Haley D, Rajagopalan R, Biswas S, Sarmady M, DeChene ET, Deardorff MA, Wilkens A, Noon SE, Scarano MI, Santani AB, White PS, Pennington J, Conlin LK, Spinner NB, Krantz ID, Vetter VL. Utility and limitations of exome sequencing as a genetic diagnostic tool for conditions associated with pediatric sudden cardiac arrest/sudden cardiac death. Hum Genomics. 2015 Jul 19;9:15.
Glessner J, Bick A, Ito K, Homsy J, Rodriguez-Murillo L, Fromer M, Mazaika E, Vardarajan B, Italia M, Leipzig J, DePalma S, Golhar R, Sanders S, Yamrom B, Ronemus M, Iossifov I, Willsey A, State M, Kaltman J, White P, Shen Y, Warburton D, Brueckner M, Seidman C, Goldmuntz E, Gelb B, Lifton R, Seidman J, Hakonarson H, Chung W. Increased frequency of de novo copy number variations in congenital heart Disease by integrative analysis of SNP array and exome sequence data. Circ Res. 2014;115:884-896.
White PS, Xie HM, Werner P, Glessner J, Latney B, Hakonarson H, Goldmuntz E. Analysis of chromosomal structural variation in patients with congenital left-sided cardiac lesions. Birth Defects Res A Clin Mol Teratol. 2014;100:951-965.
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.
D’Alessandro LCA, Werner P, Xie HM, Hakonarson H, White PS, Goldmuntz E. The prevalence of 16p12.1 microdeletion in patients with left-sided cardiac lesions. Congenit Heart Dis. 2014; 9:83-86.
Pennington JW, Ruth B, Italia MJ, Miller J, White PS. Harvest. A Web-based biomedical data discovery and reporting application development platform. J Am Med Inform Assoc. 2014; 21:379-383.
Tarczy-Hornoch P, Amendola L, Aronson SJ, Garraway L, Gray S, Grundmeier RW, Hindorff LA, Jarvik G, Karavite D, Lebo M, Plon SE, Van Allen E, Weck KE, White PS, Yang Y. A survey of informatics approaches to whole exome and whole genome clinical reporting in the electronic medical record. Genet Med. 2013; 15:824-834.
Zhang Z, Leipzig J, Sasson A, Perin JC, Xie M, Sarmady M, Warren P, White P. Efficient digest of high-throughput sequencing data in a reproducible report. BMC Bioinformatics. 2013; 14 (Suppl 11):S3.
Tropeano M, Ahn JW, Dobson RJ, Breen G, Rucker J, Dixit A, Pal DK, McGuffin P, Farmer A, White PS, Andrieux J, Vassos E, Ogilvie CM, Curran S, Collier DA. Male-biased autosomal effect of 16p13.11 copy number variation in neurodevelopmental disorders. PLOS One. 2013; 8:e61365.
Zaidi S, Choi M, Wakimoto H, Ma L, Jianming J, Overton JD, Bjornson RD, Breitbart R, Carriero NJ, Cheung YH, Deanfield J, Glessner J, Hakonarson H, Italia M, Kaltman JR, Kaski J, Kim R, Kline JK, Lee T, Leipzig J, Alexander Lopez, Mane SM, Mitchell LE, Newburger J, Pe'er I, Porter G, Roberts A, Sachidanandam R, Sanders S, Seiden HS, State M, Subramanian S, Tikhonova IR, Warburton D, Wei Z, White PS, Williams IA, Zhao H, Seidman J, Brueckner M, Chung WK, Gelb BD, Goldmuntz E, Seidman CE, Lifton RP. Increased frequency of de novo mutations in histone modifying genes in congenital heart disease. Nature. 2013; 498:220–223.
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.
Bruce J. Aronow, PhD Co-director, Computational Medicine Center
Co-director, Computational Medicine Center
Professor, UC Department of Pediatrics
Dr. Aronow 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 Aronow Lab focuses on collaborative research projects and the development of informatics systems that leverage multiple disciplines of knowledge, expertise and diverse data. The goal is to improve our collective ability to formulate high-impact inferences, hypotheses and next-stage experiments that could have the highest overall impact for biomedical research. 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. Dr. Aronow is also the first recipient of the John S. Hutton, MD, Chair for Biomedical Informatics.
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.
Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, Jegga AG, Aronow BJ, Lee CK, Bernard A, Glasser MF, Dierker DL, Menche J, Szafer A, Collman F, Grange P, Berman KA, Mihalas S, Yao Z, Stewart L, Barabási AL, Schulkin J, Phillips J, Ng L, Dang C, Haynor DR, Jones A, Van Essen DC, Koch C, Lein E. Canonical genetic signatures of the adult human brain. Nat Neurosci. 2015 Dec;18(12):1832-44.
Adams AK, Bolanos LC, Dexheimer PJ, Karns RA, Aronow BJ, Komurov K, Jegga AG, Casper KA, Patil YJ, Wilson KM, Starczynowski DT, Wells SI. IRAK1 is a novel DEK transcriptional target and is essential for head and neck cancer cell survival. Oncotarget. 2015 Oct 26.Unruh D, Srinivasan R, Benson T, Haigh S, Coyle D, Batra N, Keil R, Sturm R, Blanco V, Palascak M, Franco RS, Tong W, Chatterjee T, Hui DY, Davidson WS, Aronow BJ, Kalfa T, Manka D, Peairs A, Blomkalns A, Fulton DJ, Brittain JE,Weintraub NL, Bogdanov VY. Red Blood Cell Dysfunction Induced by High-Fat Diet: Potential Implications for Obesity-Related Atherosclerosis. Circulation. 2015 Nov 17;132(20):1898-908.
Chen S, Brunskill EW, Potter SS, Dexheimer PJ, Salomonis N, Aronow BJ, Hong CI, Zhang T, Kopan R. Intrinsic Age-Dependent Changes and Cell-Cell Contacts Regulate Nephron Progenitor Lifespan. Dev Cell. 2015 Oct 12;35(1):49-62.
Pfluger PT, Kabra DG, Aichler M, Schriever SC, Pfuhlmann K, García VC, Lehti M, Weber J, Kutschke M, Rozman J, Elrod JW, Hevener AL, Feuchtinger A, Hrabě de Angelis M, Walch A, Rollmann SM, Aronow BJ, Müller TD, Perez-Tilve D, Jastroch M, De Luca M, Molkentin JD, Tschöp MH. Calcineurin Links Mitochondrial Elongation with Energy Metabolism. Cell Metab. 2015 Nov 3;22(5):838-50.
Mrug M, Zhou J, Yang C, Aronow BJ, Cui X, Schoeb TR, Siegal GP, Yoder BK, Guay-Woodford LM. Genetic and Informatic Analyses Implicate Kif12 as a Candidate Gene within the Mpkd2 Locus That Modulates Renal Cystic Disease Severity in the Cys1cpk Mouse. PLoS One. 2015 Aug 21;10(8):e0135678.
Nayak RC, Trump LR, Aronow BJ, Myers K, Mehta P, Kalfa T, Wellendorf AM, Valencia CA, Paddison PJ, Horwitz MS, Grimes HL, Lutzko C, Cancelas JA. Pathogenesis of ELANE-mutant severe neutropenia revealed by induced pluripotentstem cells. J Clin Invest. 2015 Aug 3;125(8):3103-16.
Matrka MC, Hennigan RF, Kappes F, DeLay ML, Lambert PF, Aronow BJ, Wells SI. DEK over-expression promotes mitotic defects and micronucleus formation. Cel Cycle. 2015 May 6:1-15.
Sayed N, Wong WT, Ospino F, Meng S, Lee J, Jha A, Dexheimer P, Aronow BJ, Cooke JP. Transdifferentiation of human fibroblasts to endothelial cells: role of innate immunity. Circulation. 2015 Jan 20;131(3):300-9.
Chang KH, Sengupta A, Nayak RC, Duran A, Lee SJ, Pratt RG, Wellendorf AM, Hill SE, Watkins M, Gonzalez-Nieto D, Aronow BJ, Starczynowski DT, Civitelli R, Diaz-Meco MT, Moscat J, Cancelas JA. p62 is required for stem cell/progenitor retention through inhibition of IKK/NF-κB/Ccl4 signaling at the bone marrow macrophage-osteoblast niche. Cell Rep. 2014 Dec 24;9(6):2084-97.
John J. Hutton, MD
focuses on federated data sharing networks to support translational research, with a concentration on neuropsychiatric disorders such as bipolar spectrum disorders.
UC Department of Biomedical Informatics
John J. Hutton, MD, is the former dean of the University of Cincinnati College of Medicine and now a professor emeritus at Cincinnati Children’s. His research focuses on federated data sharing networks to support translational research, with a concentration on neuropsychiatric disorders such as bipolar spectrum disorders. Dr. Hutton oversees the department’s faculty development programs, including mentoring and peer review of drafts of research grant applications. He is also a lead participant in developing and implementing Research IT strategies that aim to improve technology cooperation and data exchange among Cincinnati Children's, UC, and UC Health.
AB: Chemistry and Physics, Harvard University, Cambridge, MA.
MD: Harvard Medical School, Boston, MA.
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 (Wash DC). 2015 Aug 17;3(1):1168.
Hutton JJ, Dexheimer P, Grabowski G. Genetic Variation and Gene Discovery. Pediatric Biomedical Informatics, ed. Springer. 2012;20:379–393.
AAMC Task Force on Information Technology. Infrastructure Requirements for Cross-Institutional Research. Challenges and Opportunities for New Collaborative Science Models. 2010;26.
Lewis CC, Aronow B, Hutton J, Santeliz J, Dienger K, Herman N, Schleifer K, Stark A, Finkelman FD, Wills-Karp M. Unique and Overlapping Gene Expression Patterns Driven by IL-4 and IL-13 in the Mouse Lung. J Allergy Clin Immunol. 2009;123:795–804.
Vukkadapu SS, Belli J, Ishii K, Jegga A, Hutton J, Aronow B, Katz J. Dynamic Interaction Between T cell-mediated Beta-cell Damage and Beta-cell Repair in the Run Up to Autoimmune Diabetes of the NOD Mouse. Physiol Genomics. 2005;21:201-211.
Guard JR, Brueggemann RF, Fant WK, Hutton JJ, Kues JR, Marine SA, Rouan GW, Schick LC. Integrated advanced information management systems: a twenty-year history at the University of Cincinnati. J Med Libr Assoc. 2004;92(2):171-8.
Hutton JJ, Jegga AG, Kong S, Gupta A, Ebert C, Williams S, Katz J, Aronow B. Microarray and comparative genomics-based identification of genes and gene regulatory regions of the mouse immune system. BMC Genomics. 2004;5:82.
Snyderman R, Hutton JJ, et al. For the Health of the Public: Ensuring the Future of Clinical Research. Report of the AAMC Task Force on Clinical Research. 2000 Jan.
Inui TS, Williams WT, Goode L, Anderson RJ, Bhak KN, Forsyth JD, Hutton JJ, Wallace AG, Dogherty RM. Sustaining the development of primary care in academic medicine. Acad Med. 1998;73:245-57.
Hutton JJ, Ess KC, Witte DP, Aronow BJ. Winner of the Theodore E. Woodward Award. c-Myb and the coordinate regulation of thymic genes. Trans AM Clin Climatol Assoc. 1996;107:115-24.
Anil Goud Jegga, DVM, MRes
Associate Professor, UC Department of Pediatrics
Anil Jegga, DVM, MRes, is a biological and medically-oriented computational biologist. The mission of 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 big data deluge into systematized knowledge to understand the molecular basis of disease. To this effect, the lab continues with their focus on integration and mining of multiple sources of genomic, genetic, and biomedical big 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 biomedical problems. The lab is currently focusing on developing and implementing systems biology-based novel computational approaches to identify drug candidates for rare diseases. Specifically, the lab is integrating and mining 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.
Patchala J, Jegga AG. Concept Modeling-based Drug Repositioning. AMIA Jt Summits Transl Sci Proc. 2015;2015:222-226.
Jegga AG. Candidate gene discovery and prioritization in rare diseases. Methods Mol Biol. 2014:1168:295-312.
Wu C, Gudivada RC, Aronow BJ, Jegga AG. Computational drug repositioning through heterogeneous network clustering. BMC Syst Biol. 2013:7 Suppl 5:S6.
Zhu C, Kushwaha A, Berman K, Jegga AG. A vertex similarity-based framework to discover and rank orphan disease-related genes. BMC Syst Biol. 2012;6 Suppl 3:S8.
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.
Sardana D, Vasa S, Vepachedu N, Chen J, Gudivada RC, Aronow BJ, Jegga AG, PhenoHM. human-mouse comparative phenome-genome server. Nucleic Acids Res. 2010;38(Web Server issue):W165-174.
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.
Chen J, Aronow BJ, Jegga AG. Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics. 2009;10:73.
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
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.
Director, Research IT Services
Assistant Professor, UC Department of Pediatrics
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.
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.
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
Dr. Lu works in bioinformatics and systems biology. He focuses on using 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. In particular, 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. He developed a network-based approach that combines proteomics experiments and computational predictions to discover the subspecies in high-density lipoprotein (HDL) cholesterol and correlate them with cardiovascular protection function. Dr. Lu has also analyzed microbial genomes to facilitate drug discovery and development. Most recently, Dr. Lu’s lab has made progress in developing computational algorithms for analyzing MRI brain images.
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.
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.
Heink A, Davidson WS, Swertfeger DK, Lu LJ, Shah AS. A Comparison of Methods To Enhance Protein Detection of Lipoproteins by Mass Spectrometry. J Proteome Res. 2015 Jul 2;14(7):2943-50.
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.
Cai WF, Liu GS, Lam CK, Florea S, Qian J, Zhao W, Pritchard T, Haghighi K, Lebeche D, Lu LJ, Deng J, Fan GC, Hajjar RJ, Kranias EG. Up-regulation of micro-RNA765 in human failing hearts is associated with post-transcriptional regulation of protein phosphatase inhibitor-1 and depressed contractility. Eur J Heart Fail. 2015 Aug;17(8):782-93.
Gordon SM, Li H, Zhu X, Shah AS, Lu LJ, Davidson WS. A comparison of the mouse and human lipoproteome: suitability of the mouse model for studies of human lipoproteins. J Proteome Res. 2015 Jun 5;14(6):2686-95.
Lu Y, Deng J, Rhodes JC, Lu H, Lu LJ. Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus. Comput Biol Chem. 2014 Jun;50:29-40.
Krishnan K, Ren Z, Losada L, Nierman WC, Lu LJ, Askew DS. Polysome profiling reveals broad translatome remodeling during endoplasmic reticulum (ER) stress in the pathogenic fungus Aspergillus fumigatus. BMC Genomics. 2014 Feb 25;15:159.
Tan L, Chen Y, Maloney TC, Caré MM, Holland SK, Lu LJ. Combined analysis of sMRI and fMRI imaging data provides accurate disease markers for hearing impairment. Neuroimage Clin. 2013 Oct 11;3:416-28.
Chen Y, Storrs J, Tan L, Mazlack LJ, Lee JH, Lu LJ. Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach. J Neurosci Methods. 2014 Jan 15;221:22-31.
Gordon SM, Deng J, Tomann AB, Shah AS, Lu LJ, Davidson WS. Multi-dimensional co-separation analysis reveals protein-protein interactions defining plasma lipoprotein subspecies. Mol Cell Proteomics. 2013 Nov;12(11):3123-34.
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.
Molecular mechanisms of gene regulation and embryonic development
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.
Liu J, Ma J. Modulation of temporal dynamics of gene transcription by activator potency in the Drosophila embryo. Development. 2015;142:3781-3790.
Cheung D, Ma J. Probing the impact of temperature on molecular events in a developmental system. Sci Rep. 2015;5:13124.
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.
Cheung D, Miles C, Kreitman M, Ma J. Adaptation of the length scale and amplitude of the Bicoid gradient profile to achieve robust patterning in abnormally large Drosophila melanogaster embryos. Development. 2014;141(1):124-135.
Liu J, Ma J. Dampened regulates the activating potency of Bicoid and the embryonic patterning outcome in Drosophila. Nat Commun. 2013;4:2968.
He F, Ren J, Wang W, Ma J. Evaluating the Drosophila Bicoid morphogen gradient system through dissecting the noise in transcriptional bursts. Bioinformatics. 2012;28:970-975.
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.
Cheung D, Miles C, Kreitman M, Ma J. Scaling of the Bicoid morphogen gradient by a volume-dependent production rate. Development. 2011;138:2741-2749.
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
Dr. Keith Marsolo is an associate professor in the Division of Biomedical Informatics within the UC Department of Pediatrics whose 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. He serves as faculty advisor for BMI Data Services, which has specialized expertise in these areas.
Dr. Marsolo led the implementation of Cincinnati Children's research data warehouse, which utilizes a custom version of the open-source i2b2 framework, allowing users to perform de-identified cohort queries. Dr. Marsolo and his team have developed several other stand-alone applications that interface with the EHR, including a tool that supports Cincinnati Children's research biobank. Dr. Marsolo and his team are also heavily involved in development of systems to support multi-center learning health systems, and in using EHR data to instantiate common data models that can be queried through distributed research networks. Dr. Marsolo and his team successfully completed an extension grant from the Agency for Healthcare Research and Quality that builds on their previous implementation of an EHR-linked registry for ImproveCareNow. Dr. Marsolo and his team are also involved in the implementation of PCORI’s National Patient-Centered Clinical Research Network (PCORnet). They are participating in a pediatric-focused Clinical Data Research Network (CDRN), and a Patient-Powered Research Network (PPRN) with ImproveCareNow. Dr. Marsolo also served as one of the co-chairs of the PCORnet’s Data Standards, Security and Network Infrastructure (DSSNI) Task Force during the first phase of PCORnet.
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.
Forrest CB, Margolis PA, Bailey LC, Marsolo K, Del Beccaro MA, Finkelstein JA, Milov DE, Vieland VJ, Wolf BA, Yu FB, Kahn MG. PEDSnet: a National Pediatric Learning Health System. J Am Med Inform Assoc. 2014 Jul;21(4):602-606.
Mandl KD, Kohane IS, McFadden D, Weber GM, Natter N, Mandel J, Schneeweiss S, Weiler S, Klann JG, Bickel J, Adams WG, Ge Y, Zhou X, Perkins J, Marsolo K, Bernstam E, Showalter J, Quarshie A, Ofili E, Hripcsak G, Murphy SN. Scalable Collaborative Infrastructure for a Learning Healthcare System (SCHILS): Architecture. J Am Med Inform Assoc. 2014 Jul;21(4):615-620.
Deleger L, Lingren T, Ni Y, Kaiser M, Stoutenborough L, Marsolo K, Kouril M, 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.
Marsolo K, Spooner SA. Clinical genomics in the world of
the electronic health record. Genet Med. 2013 Oct; 15(10):786-91.
Natter MD, Quan J, Ortiz DM, Bousvaros A, Ilowite NT, Inman
CJ, Marsolo K, McMurry AJ, Sandborg CI, Schanberg LE, Wallace CA, Warren RW,
Weber GM, Mandl KD. An i2b2-based,
Generalizable, Open Source, Self-scaling Chronic Disease Registry. J Am Med Inform Assoc. 2013 Jan 1;20(1):172-9.
Marsolo K. Informatics
& operations – let’s get integrated. J Am Med Inform Assoc. 2013 Jan 1;20(1):122-4.
Deleger L, Molnar K, Savova G, Xia F, Lingren T, Li Q,
Marsolo K, Jegga A, Kaiser M, Stoutenborough L, Solti I. Large-scale evaluation of automated clinical note de-identification and
its impact on information extraction. J
Am Med Inform Assoc. 2013 Jan
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.
Jarek Meller, PhD Graduate Program Director, Division of Biomedical Informatics
Graduate Program Director, Division of Biomedical Informatics
Associate Professor, UC Department of Environmental Health
Building upon broad interdisciplinary training in molecular modeling, computational chemistry, and bioinformatics, Dr. Meller has been pursuing research at the intersection of computational genomics and biomedicine. He 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 900,000 submissions from more than 30,000 users in many countries. He has been active in the development and applications of methods for knowledge extraction from high dimensional genomic data. Dr. Meller and his group have also been involved in many collaborative projects with direct medical relevance. Examples of such interdisciplinary efforts include sequencing and annotation of human pathogens, identification of markers associated with disease subtypes in cancer and autoimmunity, modeling of signal transduction pathways in differentiation and development, developing inhibitors of critical protein-protein interactions in autophagy, bone marrow transplants, and pathogen-host interactions.
Dr. Meller has been broadly involved in quantitative and computational training efforts within UC’s College of Medicine and College of Engineering and Applied Sciences, leading several inter-departmental and inter-collegiate initiatives in this regard. Dr. Meller serves as the director of the newly created PhD program in biomedical informatics at UC, co-director of the Biomedical Informatics Graduate Certificate Program, and has been involved in several informatics and quantitative training efforts at UC College of Medicine. Dr. Meller is the co-director of the Bioinformatics Core for the Center of Environmental Genetics at the University of Cincinnati. He also serves as the director of Protein Informatics Core at Cincinnati Children’s Hospital Medical Center.
Kesarwani M, Huber E, Kincaid Z, Evelyn CR, Biesiada J, Rance M, Thapa MB, Shah NP, Meller J, Zheng Y, Azam M. Targeting substrate-site in Jak2 kinase prevents emergence of genetic resistance. Sci Rep. 2015 Sep 30;5:14538.
Walther A, Mohanty SK, Donnelly B, Coots A, Lages CS, Lobeck I, Dupree P, Meller J, McNeal M, Sestak K, Tiao G. Rhesus rotavirus VP4 sequence-specific activation of mononuclear cells is associated with cholangiopathy in murine biliary atresia. Am J Physiol Gastrointest Liver Physiol. 2015 Sep 15;309(6):G466-74.
Chen SH, Meller J, Elber R. Comprehensive analysis of sequences of a protein switch. Protein Sci. 2015 Jun 12.
Evelyn CR, Biesiada J, Duan X, Tang H, Shang X, Papoian R, Seibel WL, Nelson S, Meller J, Zheng Y. Combined rational design and a high throughput screening platform for identifying chemical inhibitors of a Ras-activating enzyme. J Biol Chem. 2015 May 15;290(20):12879-98.
Tam NN, Zhang X, Xiao H, Song D, Levin L, Meller J, Ho SM. Increased susceptibility of estrogen-induced bladder outlet obstruction in a novel mouse model. Lab Invest. 2015 May;95(5):546-60.
Sadhasivam S, Zhang X, Chidambaran V, Mavi J, Pilipenko V, Mersha TB, Meller J, Kaufman KM, Martin LJ, McAuliffe J. Novel associations between FAAH genetic variants and postoperative central opioid-related adverse effects. Pharmacogenomics J. 2015 Oct;15(5):436-42.
Hall DP, Cost NG, Hegde S, Kellner E, Mikhaylova O, Stratton Y, Ehmer B, Abplanalp WA, Pandey R, Biesiada J, Harteneck C, Plas DR, Meller J, Czyzyk-Krzeska MF. TRPM3 and miR-204 establish a regulatory circuit that controls oncogenic autophagy in clear cell renal cell carcinoma. Cancer Cell. 2014 Nov 10;26(5):738-53.
Biesiada J, Chidambaran V, Wagner M, Zhang X, Martin LJ, Meller J, Sadhasivam S. Genetic risk signatures of opioid-induced respiratory depression following pediatric tonsillectomy. Pharmacogenomics. 2014 Nov;15(14):1749-1762.
Evelyn CR, Duan X, Biesiada J, Seibel WL, Meller J, Zheng Y. Rational design of small molecule inhibitors targeting the Ras GEF, SOS1. Chem Biol. 2014 Dec 18;21(12):1618-28.
Johansson E, Reponen T, Meller J, Vesper S, Yadav J. Association of Streptomyces community composition determined by PCR-denaturing gradient gel electrophoresis with indoor mold status. Environ Monit Assess. 2014 Dec;186(12):8773-83.
Yizhao Ni, PhD Member, Division of Biomedical Informatics
develops machine learning, natural language processing (NLP), and information retrieval techniques to assist clinical decision making.
Member, Division of Biomedical Informatics
Instructor, UC Department of Pediatrics
Clinical informatics; natural language processing; machine learning (predictive modeling)
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: (1) providing faster processing of clinical information (efficiency), (2) helping clinicians generate more objective clinical decisions (effectiveness), and (3) 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.
His current research focuses on the development of machine learning and NLP algorithms for electronic health record (EHR)-based clinical decision support. Dr. Ni already has active collaborations, including with the Divisions of Emergency Medicine, Hospital Medicine, CAGE, and Oncology at Children’s, and with Neurology and Emergency Medicine in the UC College of Medicine.
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.
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.
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.
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.
Namjou B, Ni Y, Harley IT, Chepelev I, Cobb B, Kottyan LC, Gaffney PM, Guthridge JM, Kaufman K, Harley JB. The effect of inversion at 8p23 on BLK association with lupus in Caucasian population. PLoS One. 2014;9(12):e115614.
Zhai H, Brady P, Li Q, Lingren T, Ni Y, Wheeler D, Solti I. Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children. Resuscitation. 2014;85(8):1065-1071.
Li Q, Melton K, Lingren T, Kirkendall E, Hall E, Zhai H, Ni Y, Kaiser M, Stoutenborough L, Solti I. Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care. Journal of the American Medical Informatics Association. 2014;21(5):776-784.
Li Q, Kirkendall E, Hall E, Ni Y, Lingren T, Kaiser M, Lingren N, Zhai H, Solti I, Melton K. Automated detection of medication and fluid administration errors in neonatal intensive care. Journal of Biomedical Informatics. 2015;pii:S1532-0464(15)00152-5.
John P. Pestian, PhD, MBA Director, Computational Medicine Center
Director, Computational Medicine Center
Dr. John Pestian is a professor of pediatrics, psychiatry, and biomedical informatics at Cincinnati Children's Hospital Medical Center within the University of Cincinnati. The mission of the Pestian Lab is to develop advanced technology for the care of neuropsychiatric illness. The lab does this by teaching computers to understand the dimensions of neuropsychiatric diagnosis and treatment by integrating genetics, language, voice and facial features to developing advance multi-modal analytical tools.
Dr. Pestian is the founding director of Cincinnati Children’s Division of Biomedical Informatics. He established the Computational Medicine Center with a $28 million grant from Ohio's Third Frontier Project. Ultimately, this effort led to the creation of over 500 high-tech jobs and collaborating on over $190 million in grants. He has an extensive publication and patent portfolio. One patent where he is co-inventor has been used to identify personalized neuropsychiatric medications for over 215,000 people. He is a entrepreneur who has worked with Cincinnati Children’s and CincyTech start-up experts to create companies dedicated to disseminating knowledge through the market place.
Sánchez Fernández I, Abend NS, Agadi S, An S, Arya R, Carpenter JL, Chapman KE, Gaillard WD, Glauser TA, Goldstein DB, Goldstein JL, Goodkin HP, Hahn CD, Heinzen EL, Mikati MA, Peariso K, Pestian JP, Ream M, Riviello JJ Jr, Tasker RC, Williams K, Loddenkemper T; Pediatric Status Epilepticus Research Group (pSERG). Gaps and opportunities in refractory status epilepticus research in children: a multi-center approach by the Pediatric Status Epilepticus Research Group (pSERG). Seizure. 2014 Feb;23(2):87-97.
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. 2015 Aug 7.
Shinnar S, Cnaan A, Hu F, Clark P, Dlugos D, Hirtz DG, Masur D, Mizrahi EM, Moshé SL, Glauser TA; Childhood Absence Epilepsy Study Group. Long-term outcomes of generalized tonic-clonic seizures in a childhood absence epilepsy trial. Neurology. 2015 Sep 29;85(13):1108-14.
Pestian JP. Computational semantics in clinical text supplement. Biomed Inform Insights. 2013 Jun 24;6(Suppl 1):1-2.
Pestian JP, Matykiewicz P, Linn-Gust M. What's In a Note: Construction of a Suicide Note Corpus. Biomed Inform Insights. 2012;5:1-6.
Glauser TA, Cnaan A, Shinnar S, Hirtz DG, Dlugos D, Masur D, Clark PO, Adamson PC; Childhood Absence Epilepsy Study Team. Ethosuximide, valproic acid, and lamotrigine in childhood absence epilepsy: initial monotherapy outcomes at 12 months. Epilepsia. 2013 Jan;54(1):141-55.
Pestian JP. Introductory editorial. Biomed Inform Insights. 2012;5(Suppl. 1):1.
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.
Pestian JP, Matykiewicz P, Grupp-Phelan J, Lavanier SA, Combs J, Kowatch R. Using natural language processing to classify suicide notes. AMIA Annu Symp Proc. 2008 Nov 6:1091.
Wade SL, Wolfe C, Brown TM, Pestian JP. Putting the pieces together: preliminary efficacy of a web-based family intervention for children with traumatic brain injury. J Pediatr Psychol. 2005 Jul-Aug;30(5):437-42.
Nathan Salomonis, PhD
is a genomics and bioinformatics research scientist focusing on understanding human development and genetic networks underlying disease. His lab develops computational approaches to evaluate distinct modes of gene regulation and define molecular networks that govern mammalian progenitor cell specification and human disease pathology (e.g., Sudden Infant Death Syndrome) from genome-wide experimental datasets.
Bioinformatics; genomics; alternative splicing; microRNA biology; pathway analysis; pathway visualization; pathway curation; SIDS; stem cell biology; cardiac specification; renal graft dysfunction
Our understanding of human health and ability to treat
disease is being radically transformed by new technologies to read genomes and
transcriptomes at an unprecedented resolution. To capitalize on these
technologies it is essential that we develop holistic models of gene biology
that will best inform clinicians of disease risk. Dr. Salomonis uses
computational approaches to examine the interplay between diverse modes of gene
regulation, including transcription, alternative splicing and microRNA
regulation that underlie important cellular interaction networks.
By applying such techniques to human disease and cellular
dysfunction paradigms, we strive to shed new light on existing problems. To
achieve these goals, we develop community available tools, such AltAnalyze and
GO-Elite, to analyze and interpret genome-level data that is accessible by both
untrained and skilled computational biologists alike. To identify global trends
from complex data sets, we take advantage of pathway-driven approaches, such as
WikiPathways models and aggregate large amounts of publically available data
from a broad range of developmental and disease datasets available in the
public domain. With these tools in hand, we strive to validate predicted
functional effects in the laboratory with a diverse team of collaborative
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 Oct 22;8(1):67.
Salomonis N. Systems-level perspective of sudden infant death syndrome. Pediatr Res. 2014 Sep;76(3):220-9.
Soreq L, Salomonis N, Bronstein M, Greenberg DS, Israel Z,
Bergman H, Soreq H. Small RNA
sequencing-microarray analyses in Parkinson leukocytes reveal deep brain
stimulation-induced splicing changes that classify brain region transcriptomes. Front Mol Neurosci. 2013 May 13;6:10.
Zambon AC, Gaj S, Ho I, Hanspers K, Vranizan K, Evelo CT,
Conklin BR, Pico AR, Salomonis N. GO-Elite:
a flexible solution for pathway and ontology over-representation. Bioinformatics. 2012 Aug
Salomonis N, Conklin BR. Stem cell pluripotency: alternative modes of transcription regulation. Cell Cycle. 2010 Aug 15;9(16):3133-4.
Salomonis N, Emig D*, Baumbach J, Lengauer T, Conklin BR,
Albrecht M. AltAnalyze and DomainGraph:
analyzing and visualizing exon expression data. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W755-62.
Salomonis N, Schlieve CR, Pereira L, Wahlquist C, Colas A,
Zambon AC, Vranizan K, Spindler MJ, Pico AR, Cline MS, Clark TA, Williams A,
Blume JE, Samal E, Mercola M, Merrill BJ, Conklin BR. Alternative splicing regulates mouse embryonic stem cell pluripotency
and differentiation. Proc Natl Acad
Sci U S A. 2010 Jun 8;107(23):10514-9.
Nakamura K, Salomonis N, Tomoda K, Yamanaka S, Conklin BR. G(i)-coupled GPCR signaling controls the
formation and organization of human pluripotent colonies. PLoS One. 2009 Nov 10;4(11):e7780.
Salomonis N, Nelson B, Vranizan K, Pico AR, Hanspers K,
Kuchinsky A, Ta L, Mercola M, Conklin BR. Alternative
splicing in the differentiation of human embryonic stem cells into cardiac
precursors. PLoS Comput Biol.
Stein T, Salomonis N, Nuyten DS, van de Vijver MJ, Gusterson
BA. Stein T, Salomonis N, Nuyten DS, van de Vijver MJ, Gusterson BA. A mouse mammary gland involution mRNA
signature identifies biological pathways potentially associated with breast
cancer metastasis. J Mammary Gland
Biol Neoplasia. 2009 Jun;14(2):99-116.
S. Andrew Spooner, MD, MS, FAAP Chief Medical Information Officer, Biomedical Informatics
is actively involved in patient-centered research. He is interested in clinical decision support in the pediatric electronic health record.
Chief Medical Information Officer, Biomedical Informatics
Attending Physician, Division of Hospital Medicine
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 is interested in clinical decision support in the pediatric electronic health record, especially in how accurate weight-based doses are created. Lately he has been exploring the use of statistical tools in the detection of weight data-entry errors. He and his research group have created a data warehouse focusing on medication alert data 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 our 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. He is also co-chair of the Certification Commission for Health Information Technology inpatient work group.
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.
Spooner SA. We are still waiting for fully supportive electronic health records in pediatrics. Pediatrics. 2012 Dec;130(6):e1674-6.
Spellman Kennebeck S, Timm N, Farrell MK, Spooner SA. Impact of electronic health record implementation on patient flow metrics in a pediatric emergency department. J Am Med Inform Assoc. 2012 May-Jun;19(3):443-7.
Spooner SA, Classen DC. Data standards and improvement of quality and safety in child health care. Pediatrics. 2009 Jan;123 Suppl 2:S74-9.
Michael Wagner, PhD Faculty Liaison, Biomedical Informatics Core
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.
Faculty Liaison, Biomedical Informatics Core
Large-scale optimization; applications in bioinformatics
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 example, he is co-principal investigator on the Longitudinal Pediatric Data Resource (LPDR) project funded through the Newborn Screening Translational Research Network (NBSTRN). The LPDR will be used by researchers nationwide to mine health outcome data over the lifespan of children who screen positive for a large number of rare and often devastating genetic disorders.
Dr. Wagner is also overseeing the development of genomic data management and analysis tools both for the Center for Pediatric Genomics (CpG) at Cincinnati Children's and in his role as director of the Rheumatic 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.
Watson J, Kinstler A, Vidonish WP 3rd, Wagner M, Lin L, et al. Impact of Noise on Nurses in Pediatric Intensive Care Units. Am J Crit Care. 2015;24(5):377-84.
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.
Patel ZH, Kottyan LC, Lazaro S, Williams MS, Ledbetter DH, et al. The struggle to find reliable results in exome sequencing data: filtering out Mendelian errors. Frontiers in genetics. 2014;5:16.
Morrow AL, Lagomarcino AJ, Schibler KR, Taft DH, Yu Z, et al. Early microbial and metabolomic signatures predict later onset of necrotizing enterocolitis in preterm infants. Microbiome. 2013;1(1):13.
Namjou B, Keddache M, Marsolo K, Wagner M, Lingren T, et al. EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children. Frontiers in genetics. 2013;4:268.
Thompson SD, Marion MC, Sudman M, Ryan M, Tsoras M, et al. Genome-wide association analysis of juvenile idiopathic arthritis identifies a new susceptibility locus at chromosomal region 3q13. Arthritis and rheumatism. 2012;64(8):2781-91.
Huang SH, Mo D, Meller J, Wagner M. Identifying a small set of marker genes using minimum expected cost of misclassification. Artificial intelligence in medicine. 2012;55(1):51-9.
Biesiada J, Sadhasivam S, Wagner M, Meller J. From SNP Genotyping to Improved Pediatric Healthcare. Pediatric Biomedical Informatics: Computer Applications in Pediatric Research. Hutton JJ, editor. New York:Springer;2012.
Kouril M, Hunt N, Wagner M. Data Storage and Access Management. Pediatric Biomedical Informatics: Computer Applications in Pediatric Research. Hutton JJ, editor. New York:Springer;2012.
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.
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.
Medical informatics; clinical decision support; artificial intelligence
Judith Dexheimer, PhD, 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.
PhD: Biomedical Informatics, Vanderbilt University, Nashville, TN, 2011.
MS: Biomedical Informatics, Vanderbilt University, Nashville, TN, 2006.
Kurowski BG, Wade S, Dexheimer JW, Dyas J, Zhang N, Babcock L. Feasibility and potential benefits of a web-based intervention delivered acutely after mild traumatic brain injury in adolescents: a pilot study. Journal of Head Trauma Rehabilitation. 2015.
Borycki E, Cummings E, Dexheimer JW, Gong Y, Kennebeck S, Kushniruk A, Kuziemsky C, Saranto K, Weber JH, Takeda H. Patient-centered Coordinated Care in Times of Emerging Diseases and Epidemics. Yearb Med Inform. 2015 Aug 13;10(1):207-15.
Dexheimer JW, Scheid B, Babaoff A, Martens S, Kennebeck S. Preparing for International Classification of Diseases, 10th revision, clinical modification implementation: strategies for maintaining an efficient workflow. Pediatr Emer Care. 2015;31(1):65-69.
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, Borycki EM. Use of mobile devices in the emergency department: A scoping review. Health Informatics J. 2015 Dec;21(4):306-15.
Dexheimer JW, Borycki EM, Chiu K, Johnson KB, Aronsky D. A systematic review of the implementation and impact of asthma protocols. BMC Med Inform Decis Mak. 2014;14:82.
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.
Dixon CA, Ammerman RT, Dexheimer JW, Meyer B, Jung H, Johnson BL, Elliott J, Jacobs T, Pomerantz WJ, Mahabee-Gittens EM. Development of iBsafe: A Collaborative, Theory-based Approach to Creating a Mobile Game Application for Child Safety. AMIA Annu Symp Proc. 2014 Nov 14;2014:477-85.
Dexheimer JW, Kennebeck S. Modifications and Integration of the Electronic Tracking Board in a Pediatric Emergency Department. Pediatr Emer Care. 2013;29(7):852-7.
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.
Biomedical informatics; clinical informatics; data linkage; infant mortality; maternal child health; neonatal abstinence syndrome; preterm birth; public health informatics
Eric S. Hall, PhD, is a biomedical informaticist and researcher focused on improving perinatal health outcomes in at-risk populations. Dr. Hall currently leads data management and integration supporting research and quality improvement within the Cincinnati Children's Hospital Medical Center Perinatal Institute.
Dr. Hall's primary interests involve the linkage and integration of disparate data sets to facilitate the analysis of high-risk populations. These maternal and child health data include hospital electronic health records, vital records, geospatially-based measures from the American Communities Survey and other public data resources, and measures collected by community-based programs. While his principal focus has been on preterm birth and infant mortality reduction, Dr. Hall's interests extend to other high-risk maternal and child populations including those affected by withdrawal following in utero exposure to narcotics.
Dr. Hall has participated as a key member of many interdisciplinary teams within the Perinatal Institute and as part of multi-institutional collaborations. He is also the director of data analysis and management for Cradle Cincinnati, a collective impact collaborative to reduce infant mortality in Hamilton County, Ohio.
PhD: University of Utah, Salt Lake City, UT, 2008.
MS: Brigham Young University, Provo, UT, 2003.
BS: Brigham Young University, Provo, UT, 2001.
Li Q, Kirkendall ES, Hall ES, Ni Y, Lingren T, Kaiser M, Lingren N, Zhai H, Solti I, Melton K. Automated Detection of Medication Administration Errors in Neonatal Intensive Care. J Biomed Inform. 2015 Jul 16.
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, Folger AT, Kelly EA, Kamath-Rayne BD. Evaluation of gestational age estimate method on the calculation of preterm birth rates. Matern Child Health J. 2014 Apr;18(3):755-62.
Goyal NK, Hall ES, Ammerman RT, Meinzen-Derr JK, Jones DE, Short JA, Van Ginkel JB. Association of maternal and community factors with enrollment in home visiting among at-risk, first time mothers. Am J Public Health. 2014 Feb;104 Suppl 1:S144-51.
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.
Goyal NK, Hall ES, Meinzen-Derr JK, Kahn RS, Short JA, Van Ginkel JB, Ammerman RT. Dosage effect of prenatal home visiting on pregnancy outcomes in at-risk, first-time mothers. Pediatrics. 2013 Nov;132 Suppl 2:S118-25.
South AP, Jones DE, Hall ES, Huo S, Meinzen-Derr J, Liu L, Greenberg JM. Spatial analysis of preterm birth demonstrates opportunities for targeted intervention. Matern Child Health J. 2012 Feb;16(2):470-8.
Hall ES, Poynton MR, Narus SP, Jones SS, Evans RS, Varner MW, Thornton SN. Patient-level analysis of outcomes using structured labor and delivery data. J Biomed Inform. 2009 Aug;42(4):702-9.
Hall ES, Poynton MR, Narus SP, Thornton SN. Modeling the distribution of Nursing Effort using structured Labor and Delivery documentation. J Biomed Inform. 2008 Dec;41(6):1001-8.
Hall ES, Vawdrey DK, Knutson CD, Archibald JK. Enabling remote access to personal electronic medical records. IEEE Eng Med Biol Mag. 2003 May-Jun;22(3):133-9.
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.
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 results 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.
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, 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.
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, 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.
Eric S. Kirkendall, MD, MBI, FAAP Associate Chief Medical Information Officer, Information Services
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.
Associate Chief Medical Information Officer, Information Services
Attending Hospitalist, Division of Hospital Medicine
Clinical Informaticist, Division of Biomedical Informatics
Eric Kirkendall, MD, MBI, is an associate professor of Pediatrics at Cincinnati Children’s Hospital Medical Center within the University of Cincinnati College of Medicine. He is the first Associate CMIO in Cincinnati Children's history and oversees the design, implementation, and optimization of the electronic health record and other associated technologies.
From a research perspective, 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. These techniques are innovative because they allow investigators to use unstructured data that would otherwise be unavailable for analysis. The results have shown vast improvements in detecting errors related to medication administrations. This work is currently ongoing and is supported by R01 grant funding from the National Library of Medicine.
Pediatric acute kidney injury (AKI) results from needless exposure to nephrotoxic medications (NTMx). At Cincinnati Children's, Dr. Kirkendall 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. He has led the development of these triggers, as well as development of a custom application that is key to data collection and improvement methods. This work has spread to 9 other sites, with spread to approximately 80 more sites planned.
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.
Li Q, Kirkendall ES, Hall ES, Ni Y, Lingren T, Kaiser M, Lingren N, Zhai H, Solti I. Automated Detection of Medication Administration Errors in Neonatal Intensive Care. Journal of Biomedical Informatics. 2015 Jul 15.
Stockwell DC, Bisarya H, Classen DC, Kirkendall ES, Landrigan CP, Lemon V, Tham E, Hyman D, Lehman SM, Searles E, Hall M, Muething SE, Sharek PJ. A Trigger Tool to Detect Harm in Pediatric Inpatient Settings. Pediatrics. 2015 Jun;135(6)1036-42.
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.
Stockwell DC, Bisarya H, Classen DC, Kirkendall ES, Lachman PI, Matlow AG, Tham E, Hyman D, Lehman SM, Searles E, Muething SE, Sharek PJ. Development of an Electronic Pediatric All-Cause Harm Measurement Tool Using the Modified Delphi Method. Journal of Patient Safety. 2014 Aug 26.
Kirkendall ES, Spires WL, Mottes TA, Schaffzin JK, Barclay C, Goldstein SL. Development and Performance of Electronic Acute Kidney Injury Triggers to Identify Pediatric Patients at Risk for Nephrotoxic Medication-associated Harm. Applied Clinical Informatics. 2014 Apr 2;5(2):313-33.
Li Q, Melton K, Lingren T, Kirkendall ES, Hall E, Zhai H, Ni Y, Kaiser M, Stoutenborough L, Solti I. Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care. J Am Med Inform Assoc. 2014 Sep;21(5):776-84.
Kirkendall ES, Kouril M, Minich T, Spooner SA. Analysis Of Electronic Medication Orders With Large Overdoses: Opportunities For Mitigating Dosing Errors. Applied Clinical Informatics. 2014;5(1):25-45.
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
Kirkendall ES, Spooner SA, Logan JR. Evaluating the accuracy of electronic pediatric drug dosing rules. J Am
Med Inform Assoc. 2013 Jun 28.
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.
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, Cancer Biology and Neural Tumors Program
Member, Cancer Biology and Neural Tumors Program
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.
Dr. Komurov focuses on computational and systems biology of cancer. His research interests are centered on integrating in silico, in vitro and in vivo analyses to model and target cancer cell vulnerabilities. Research in the Komurov lab also involves development of new computational tools, including methodology and software, for effective integration of large ‘omics and prior functional interaction datasets for knowledge-based data analyses.
Adams AK, Bolanos LC, Dexheimer PJ, Karns RA, Aronow BJ, Komurov K, Jegga AG, Casper KA, Patil YJ, Wilson KM, Starczynowski DT, Wells SI. IRAK1 is a novel DEK transcriptional target and is essential for head and neck cancer cell survival. Oncotarget. 2015 Oct 26.
Segura-Cabrera A, Singh N, Komurov K. An integrated network platform for contextual prioritization of drugs and pathways. Mol Biosyst. 2015 Oct 13;11(11):2850-9.
Singh N, Joshi R, Komurov K. HER2-mTOR signaling-driven breast cancer cells require ER-associated degradation to survive. Sci Signal. 2015 May 26;8(378):ra52.
Gopal YN, Rizos H, Chen G, Deng W, Frederick DT, Cooper ZA, Scolyer RA, Pupo G, Komurov K, Sehgal V, Zhang J, Patel L, Pereira CG, Broom BM, Mills GB, Ram P, Smith PD, Wargo JA, Long GV, Davies MA. Inhibition of mTORC1/2 overcomes resistance to MAPK pathway inhibitors mediated by PGC1α and oxidative phosphorylation in melanoma. Cancer Res. 2014 Dec 1;74(23):7037-47.
Fang J, Barker B, Bolanos L, Liu X, Jerez A, Makishima H, Christie S, Chen X, Rao DS, Grimes HL, Komurov K, Weirauch MT, Cancelas JA, Maciejewski JP, Starczynowski DT. Myeloid malignancies with chromosome 5q deletions acquire a dependency on an intrachromosomal NF-κB gene network. Cell Rep. 2014 Sep 11;8(5):1328-38.
Kim HS, Mendiratta S, Kim J, Pecot CV, Larsen JE, Zubovych I, Seo BY, Kim J, Eskiocak B, Chung H, McMillan E, Wu S, De Brabander J, Komurov K, Toombs JE, Wei S, Peyton M, Williams N, Gazdar AF, Posner BA, Brekken RA, Sood AK, Deberardinis RJ, Roth MG, Minna JD, White MA. Systematic identification of molecular subtype-selective vulnerabilities in non-small-cell lung cancer. Cell. 2013 Oct 24;155(3):552-66.
Matsuo K, Nishimura M, Komurov K, Shahzad MM, Ali-Fehmi R, Roh JW, Lu C, Cody DD, Ram PT, Loizos N, Coleman RL, Sood AK. Platelet-derived growth factor receptor alpha (PDGFRα) targeting and relevant biomarkers in ovarian carcinoma. Gynecol Oncol. 2014 Jan;132(1):166-75.
Lane A, Segura-Cabrera A, Komurov K. A comparative survey of functional footprints of EGFR pathway mutations in human cancers. Oncogene. 2014 Oct 23;33(43):5078-89.
Ward SE, Kim HS, Komurov K, Mendiratta S, Tsai PL, Schmolke M, Satterly N, Manicassamy B, Forst CV, Roth MG, García-Sastre A, Blazewska KM, McKenna CE, Fontoura BM, White MA. Host modulators of H1N1 cytopathogenicity. PLoS One. 2012;7(8):e39284.
Komurov K, Tseng JT, Muller M, Seviour EG, Moss TJ, Yang L, Nagrath D, Ram PT. The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells. Mol Syst Biol. 2012;8:596.
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.
Alexey Porollo, PhD Member, Center for Autoimmune Genomics and Etiology
Member, Center for Autoimmune Genomics and Etiology
UC Department of Environmental Health
Alexey Porollo, PhD, 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.
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).
Fechter K, Porollo A. MutaCYP: Classification of missense mutations in human cytochromes P450. BMC Med Genomics. 2014 Jul 30;7(1):47.
Subramanian Vignesh K, Landero Figueroa JA, Porollo A,
Caruso JA, Deepe GS Jr. Granulocyte
macrophage-colony stimulating factor induced Zn sequestration enhances macrophage
superoxide and limits intracellular pathogen survival. Immunity. 2013 Oct
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
Green JV, Orsborn KI, Zhang M, Tan QK, Greis KD, Porollo A, Andes DR, Long Lu J, Hostetter MK. Heparin-binding motifs and biofilm formation by Candida albicans. J Infect Dis. 2013 Nov 15;208(10):1695-704.
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 PKCζ in tumorigenesis. Cell.
2013 Jan 31;152(3):599-611.
Porollo A, Meller J, Joshi Y, Jaiswal V, Smulian AG, Cushion MT. Analysis of current antifungal agents and their targets within the Pneumocystis carinii genome. Curr Drug Targets. 2012 Nov;13(12):1575-85.
Duran A, Amanchy R, Linares JF, Joshi J, Abu-Baker S,
Porollo A, Hansen M, Moscat J, Diaz-Meco MT. p62
is a key regulator of nutrient sensing in the mTORC1 pathway. Mol Cell. 2011 Oct 7;44(1):134-46.
Porollo A, Meller J. Prediction-based
fingerprints of protein-protein interactions. Proteins. 2007 Feb 15;66(3):630-45.
Adamczak R, Porollo A, Meller J. Accurate prediction of solvent accessibility using neural
networks-based regression. Proteins.
2004 Sep 1;56(4):753-67.
Directed Culturing of
Pneumocystis Using Metatranscriptomics. Co-Principal Investigator. National
Institutes of Health: Heart, Lung and Blood Institute. May 2013-Feb 2018. R01HL119190-01.
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
is interested in radiology informatics; cancer imaging and abdominal imaging.
Radiologist, Department of Radiology and Medical Imaging
Neil D. Johnson Chair, Radiology Informatics
Associate Chief, Radiology Informatics
Co-Chief, Thoracoabdominal Imaging
Associate Professor, UC Department of Radiology
Radiology informatics; cancer imaging; abdominal imaging
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.
Trout AT, Zhang B, Care MM, Towbin AJ. The striated MR nephrogram, not a reflection of pathology. Pediatric radiology. 2015;45(11):1644-50.
Leung DH, Ye W, Molleston JP, Weymann A, Ling S, et al. Baseline Ultrasound and Clinical Correlates in Children with Cystic Fibrosis. The Journal of pediatrics. 2015;167(4):862-868.e2.
Trout AT, Towbin AJ, Fierke SR, Zhang B, Larson DB. Appendiceal diameter as a predictor of appendicitis in children: improved diagnosis with three diagnostic categories derived from a logistic predictive model. European radiology. 2015;25(8):2231-8.
Wallihan DB, Podberesky DJ, Sullivan J, Denson LA, Zhang B, et al. Diagnostic Performance and Dose Comparison of Filtered Back Projection and Adaptive Iterative Dose Reduction Three-dimensional CT Enterography in Children and Young Adults. Radiology. 2015;276(1):233-42.
Anupindi SA, Podberesky DJ, Towbin AJ, Courtier J, Gee MS, et al. Pediatric inflammatory bowel disease: imaging issues with targeted solutions. Abdominal imaging. 2015;40(5):975-92.
Bunt CW, Burke HB, Towbin AJ, Hoang A, Stephens MB, et al. Point-of-Care Estimated Radiation Exposure and Imaging Guidelines Can Reduce Pediatric Radiation Burden. Journal of the American Board of Family Medicine: JABFM. 2015;28(3):343-50.
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.
Shaughnessy EE, Towbin A, Prosser J. Neonate with choking. JAMA pediatrics. 2015;169(3):281-2.
Kolbe AB, Podberesky DJ, Zhang B, Towbin AJ. The impact of hepatocyte phase imaging from infancy to young adulthood in patients with a known or suspected liver lesion. Pediatric radiology. 2015;45(3):354-65.
Cripe TP, Ngo MC, Geller JI, Louis CU, Currier MA, et al. Phase 1 study of intratumoral Pexa-Vec (JX-594), an oncolytic and immunotherapeutic vaccinia virus, in pediatric cancer patients. Molecular therapy: the journal of the American Society of Gene Therapy. 2015;23(3):602-8.
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.
Transcription factors; transcriptional regulation; functional genomics; genome analysis
Matthew T. Weirauch, PhD, a faculty member in the Center for Autoimmune Genomics and Etiology (CAGE), is a computational biologist with special emphasis on genomic approaches for studying transcription factor (TF) interactions with DNA, and how genetic variation proximal to these interactions contributes to human diseases. He recently spearheaded large-scale efforts for the experimental and computational determination of sequence binding motifs for eukaryotic TFs (Weirauch et al., Cell, 2014), and RNA binding proteins (Nature co-first author, 2013). He has also been involved in numerous high-profile genomics efforts, including an evaluation of algorithms for TF-DNA recognition (Weirauch et al., Nature Biotech, 2013), and the largest genetic interaction studies performed to date in both C. elegans (Byrne et al., Journal of Biology, 2007) and S. cerevisiae (Costanzo et al., Science, 2010). Recent work in his group focuses on how disease-associated genomic regions affect TF binding, and how these alterations affect disease onset and progression (Wang et al., Mol Cancer., 2010; Qian et al., Pediatr Blood Cancer, 2014; Kottyan et al., Nature Genetics, 2014; Martin et al., Circulation: Cardiovascular Genetics, 2014; Fang et al., Cell Reports, 2014).
The long-term goal of Dr. Weirauch's lab is to create an accurate computational system for predicting TF and RNA binding protein interactions with the genome/transcriptome, and for understanding the effects of genetic variations on these interactions. As it continues to mature, they are applying this system to predict the effects of genetic variants that are strongly associated with several human diseases. Long-term, they envision that this system will be used for personalized medicine-based approaches – given the genome sequence of a patient, it will produce a prioritized list of genetic variants likely to contribute to disease onset via alterations to protein binding events.
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.
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.
Makashir S, Kottyan L, Weirauch M. Meta-analysis of Differential Gene Co-expression: Application to Lupus. Pacific Symposium on Biocomputing. 2014.
Kottyan L, Davis B, Sherrill J, Lui K, Rochman M, Kaufman K, Weirauch M, Vaughn S, Lazaro S, Rupert A, Kohram M, Stucke E, Kemme K, Magnusen A, He H, Dexheimer P, Mukkada V, Putnam P, Strauss A, Abonia JP, Martin L, Harley J, Rothenberg M. Genome-wide association analysis of eosinophilic esophagitis provides insight into the tissue specificity of this allergic disease. Nat Genet. 2014 Aug;46(8):895-900.
Sullivan A, Arsovski A, Lempe J, Bubb K, Weirauch M, et al. Mapping and Dynamics of Regulatory DNA and Transcription Factor Networks in A. thaliana.
Cell Reports. 2014 Sep 10.
Kottyan L, Zoller E, Bene J, Lu X, Kelly J, Rupert A, Lessard C, Vaughn S, Marion M, Weirauch M, et al. The IRF5-TNPO3 association with systemic lupus erythematosus (SLE) has two components that other autoimmune disorders variably share.
Hum Mol Genet. 2014 Sep 8.
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.
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.
Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear E, Sevier C, Ding H, Koh J, Toufighi K, Mostafavi S, Prinz J, St Onge R, VanderSluis B, Makhnevych T, Vizeacoumar F, Alizadeh S, Bahr S, Brost R, Chen Y, Cokol M, Deshpande R, Li Z, Lin Z, Liang W, Marback M, Paw J, San Luis B, Shuteriqi E, Tong A, van Dyk N, Wallace I, Whitney J, Weirauch M, Zhong G, Zhu H, Houry W, Brudno M, Ragibizadeh S, Papp B, Pál C, Roth F, Giaever G, Nislow C, Troyanskaya O, Bussey H, Bader G, Gingras A, Morris Q, Kim P, Kaiser C, Myers C, Andrews B, Boone C. The genetic landscape of a cell. Science. 2010 Jan 22;327(5964):425-31.
Byrne A, Weirauch M, Wong V, Koeva M, Dixon S, Stuart J, Roy P. A global analysis of genetic interactions in Caenorhabditis elegans.
J Biol. 2007;6(3):8.
ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.
Nature. 2007 Jun 14;447(7146):799-816.
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.
Yan Xu, PhD Director of Bioinformatics Core, Neonatology & Pulmonary Biology; Perinatal Institute
has research interests that 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.
Director of Bioinformatics Core, Neonatology & Pulmonary Biology; Perinatal Institute
Bioinformatics; systems biology application in pulmonary research; lung cell type specific signaling; asthma associated pathology
Dr. Xu's research interests are 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.
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.
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.
Bridges JP, Schehr A, Wang Y, Huo L, Besnard V, Ikegami M, Whitsett JA, Xu Y. Epithelial SCAP/INSIG/SREBP Signaling Regulates Multiple Biological Processes During Perinatal Lung Maturation. PLoS One. 2014 May 7;9(5):e91376.
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, Whitsett JA. Functional Genomics - Transcriptional Networks Controlling Lung Maturation and Surfactant Homeostasis. Hutton J (Ed). Pediatric Biomedical Informatics: Computer Applications in Pediatric Research (Translational Bioinformatics). 2012. Publisher: Springer.
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.
Xu Y, Saegusa C, Schehr A, Grant S, Whitsett JA, Ikegami M. C/EBPa is required for pulmonary cytoprotection during hyperoxia. American journal of physiology. 2009;297(2):L286-298.
Xu Y, Ikegami M, Wang Y, Matsuzaki Y, Whitsett JA. Gene expression and biological processes influenced by deletion of Stat3 in pulmonary type II epithelial cells. BMC Genomics. 2007;8:455.
Xu Y, Liu C, Clark JC, Whitsett JA. Functional genomic responses to cystic fibrosis transmembrane conductance regulator (CFTR) and CFTR(508) in the lung. J Biol Chem. 2006;281:11279-11291.
Wan H, Xu Y, Ikegami M, Stahlman MT, Kaestner KH, Ang SL, Whitsett JA. Foxa2 is required for transition to air breathing at birth. Proc Natl Acad Sci USA. 2004;101:14449-14454.
Xu Y, Clark JC, Aronow BJ, Dey CR, Liu C, Wooldridge JL, Whitsett JA. Transcriptional adaptation to cystic fibrosis transmembrane conductance regulator deficiency. J Biol Chem. 2003;278:7674-7682.
“Lung Map” Atlas Research Center. Co-Investigator. National Heart, Lung, and Blood Institute. Jan 2014-Jan 2019.
Transcriptional Programming of Asthma Related Pathology in Respiratory Epithelial. Co-Investigator. National Heart, Lung, and Blood Institute. Apr 2013-Mar 2018.
Airway Progenitor Cell Proliferation and Differentiation During Lung Repair. Co-Investigator. National Heart, Lung, and Blood Institute. Jan 2012-Dec 2016.
Mechanisms Underlying DICER1 Suppression of Pleuropulmonary Blastoma. Co-Investigator. St. Baldrick's Foundation. Jul 2011-Jun 2016.
Stard7, a Novel Inhibitor of Allergic Lung Disease. Co-Investigator. National Institutes of Health/National Heart, Lung, and Blood Institute. Dec 2013-Nov 2018. HL122130.
Matrix fibroblasts are required for alveolar homeostasis and regrowth. Co-Investigator. National Institutes of Health/National Heart, Lung, and Blood Institute. Sep 2015-Aug 2016. R56 HL123969.
Transcriptional regulation of pulmonary fibrosis. Co-Investigator. National Institutes of Health/National Heart, Lung, and Blood Institute. Sep 2015-Aug 2016. R56 HL126660.
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