Bruce J. Aronow, PhD

Dr. Aronow, PhD, is a geneticist and computational and developmental biologist. His group carries out analyses, develops algorithms, and builds web databases that allow researchers from varying disciplines and backgrounds to access diverse types of biological data to better understand, model, and research complex biological processes in order to cure disease. The Aronow-Jegga Lab is highly collaborative with clinical and basic researchers involved in 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 based on genetic, genomic, proteomic, metabolomic and imaging data along with therapeutic agent response measures. Areas of high current interest include large-scale clinical sample analyses, single cell-based tissue dissection, and in vitro stem cell-based modeling of normal and abnormal neurological, immunological, cardiac, and cancer tissues and the prediction of new therapeutic approaches based on disease mechanism analysis. The labs recent efforts are focusing on the areas of inflammatory bowel disease, eosphinophilic esophagitis, sickle cell anemia, and neurological and psychiatric diseases; defining the transcriptome of the developing kidney, lung and brain; using stem cell-derived cells and organoids; and better understanding mechanisms that underlie cancers and the actions of therapeutic agents against disease.

Anil Goud Jegga, DVM, MRes

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. To this effect, 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 have been designed to answer several straightforward questions that biologists frequently encounter while trying to apply systems-level analyses to specific biological problems. The lab is currently focusing on developing and implementing systems biology-based novel computational approaches to identify drug candidates for rare lung disorders. The lab is integrating and mining genomic and compound screening-based big data to identify drug repositioning and novel drug candidates.

Michal Kouril, PhD

Dr. Kouril, PhD, 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 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.

Long (Jason) Lu, PhD

Dr. Lu focuses on developing innovative computational approaches to study a variety of human diseases. 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. 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. Dr. Lu has also introduced a new perspective to characterize gene essentiality from protein domains, the independent structural or functional units of a polypeptide chain. Research identifies genes with indispensable functions as essential; however, the traditional gene-level studies of essentiality have several limitations. To identify such essential domains, Dr. Lu's lab developed an Expectation-Maximization (EM) algorithm-based Essential Domain Prediction (EDP) Model and presented the first systematic analysis on gene essentiality on the level of domains. In another research direction, Dr. Lu's lab developed a new computer program that analyzes functional brain MRIs in hearing-impaired children before cochlear implant and predicts whether they will develop effective language skills after surgery. The prediction algorithm, based on semi-supervised machine learning, can also evaluate how specific regions of the brain respond to auditory stimulus tests that hearing-impaired infants and toddlers receive before surgical implantation. With additional research and development, the computer model could become a practical tool that allows clinicians to reduce the number of children who undergo an invasive and costly procedure, only to be disappointed when implants do not deliver hoped-for results.

Jun Ma, PhD

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 received support grants from the National Institutes of Health (NIH) and the National Science Foundation (NSF).

Keith Marsolo, PhD

Dr. Marsolo, and the learning networks informatics team, successfully completed an extension grant from the Agency for Healthcare Research and Quality (AHRQ) that builds on their implementation of an electronic health record (EHR)-linked registry for the ImproveCareNow Network, a 92-center quality improvement and research network that focuses on improving the care and outcomes of children with inflammatory bowel disease (IBD). Data on 75% of the patients in the network (at the time of submission) are now being automatically uploaded to the registry, which has significantly reduced the overall data entry burden for the network. Marsolo’s team is participating in three projects that are part of the Patient Centered Outcomes Research Institute’s (PCORI) National Patient-Centered Clinical Research Network (PCORnet). They are part of two Clinical Data Research Network (CDRN) awards, as well as a Patient-Powered Research Network (PPRN). Among the various required tasks of these awards, Dr. Marsolo and his team will create standardized extracts of EHR data for Cincinnati Children's and ImproveCareNow patients, and use that information to respond to analytical queries developed by patients and investigators within PCORnet. This network will also be used to identify and recruit patients for clinical trials and to conduct observational and comparative effectiveness research. In addition, Dr. Marsolo is serving as one of the co-chairs of the PCORnet’s Data Standards, Security and Network Infrastructure (DSSNI) Task Force.

Yiazhao Ni, PhD

Dr. Ni’s team consists of experts in natural language processing (NLP), machine learning (ML) and information retrieval (IR). Leveraging NLP and IR technologies, the team successfully developed a prototype automated system for patient eligibility screening. Utilizing advanced ML methodology, they also developed an automated algorithm to predict patients’ responses to clinical trial invitations to facilitate patient recruitment. Dr. Ni was awarded a grant under the Cincinnati Children's Innovation Fund to continue this line of research. Dr. Ni’s team also develops electronic health record (EHR)-based data analytics to support clinical decision-making. His team has participated in a variety of research projects: 1) the Electronic Medical Records and Genomics Network (eMERGE, U01) project, where Dr. Ni and his team explored the EHR information and developed phenotype algorithms for specific diseases; 2) the NICU medication safety project (R01), where the team automated medication discrepancy detection between patients’ medication administrations and medication orders; and 3) the sustainable surveillance of diabetes project (U18), where the team used NLP and ML technologies to ascertain if a patient had diabetes, and if so, the date of diagnosis. In addition to his research, Dr. Ni is serving as a ML specialist in multiple quality improvement projects such as the safety and situation awareness project.

John P. Pestian, PhD, MBA

Dr. Pestian's lab focuses on using the science of natural language understanding in biomedical settings with the goal of developing advanced technology for the care of neuropsychiatric illness. Recently, they have begun to integrate genetics, language, voice and facial features for identifying epilepsy surgery candidates and those at risk for depression, anxiety and suicide. The lab's epilepsy studies focuses on building computer systems used to understand the quality of epilepsy care. They have also developed methods for predicting neurosurgery for epileptic patients. The goal of their suicide research is to develop a system that will identify suicidal adolescents. Here they are trying to combine the linguistic, acoustic and visual cues that will help a clinician decide if an adolescent will attempt suicide. Some of Cincinnati Public Schools are currently testing these algorithms. Recent highlights for Dr. Pestian, and his lab, include the issuance five patents. The Pestian Lab works with local and national entrepreneurs to disseminate their findings to the marketplace. From these collaborations, more than 500 jobs and $255 million have been generated. One invention, Optimization and Individualization of Medication Selection and Dosing, is used for optimal mental health drug selection on over 400,000 people. The other notable invention, Processing Text With Domain-Specific Spreading Activation Methods, is used for identification of suicide, and other mental illness, using “thought markers” like language, acoustics and facial characteristics. 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.

Nathan Salomonis, PhD

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.

S. Andrew Spooner, MD, MS, FAAP

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.

Michael Wagner, PhD

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 (NHLBI)-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 (NBSTRN) and Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The LPDR, used by researchers nationwide, mines 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, funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS).

Peter S. White, PhD

Dr. White is the director of the Division of Biomedical Informatics at Cincinnati Children's, and the Rieveschl Chair of the Department of Biomedical Informatics at the University of Cincinnati College of Medicine. Dr. White also serves as co-director of Cincinnati Children’s Center for Pediatric Genomics. In these roles, he oversees informatics research and resources at both institutions, including academic, educational, data services, technology development and research IT missions. 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 methods and technologies for integration and use of clinical, phenotypic and molecular data in promoting discovery and hypothesis validation. Dr. White is playing a lead informatics role on a number of national data networks, including the Eunice Kennedy Shriver National Institute of Child Health and Human Development's (NICHD) Newborn Screening Translational Research Network, the National Heart, Lung, and Blood Institute's (NHLBI) Bench to Bassinet Program, and the Genomic Research and Innovation Network.