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Professor, Biomedical Informatics.
Dr. Aronow is a computational geneticist and developmental biologist. His group carries out analyses of many different kinds of data, develops algorithms, and builds websites and databases. These tools allow researchers from varying disciplines and backgrounds to analyze genetic and genomic data—either their own or that gathered from published sources—to better understand, model, and carry out new research about normal development and disease. His group is highly collaborative with clinical and basic researchers across a broad range of research projects encompassing many areas of biology and disease, including normal and abnormal development, in vivo and in vitro disease models, and large-scale clinical studies. Data of interest includes genetic, genomic, proteomic, metabolomic, imaging, and therapeutic agent response measures. Recent areas of interest include large-scale clinical sample analyses, single cell-based dissection of developmental and disease tissues, and in vitro stem cell-based modeling of normal and disease-affected tissues including abnormal neurological, immunological, cardiac, and cancer tissues. The lab’s recent efforts focus on predicting new therapeutic approaches based on disease mechanisms in the areas of inflammatory bowel disease, eosinophilic esophagitis, sickle cell anemia, cardiac development, and neurological and psychiatric diseases. His group is working on efforts to define the transcriptome of the developing kidney, lung and brain. They are using stem cell-derived cells and organoids to dissect mechanisms that underlie organ development and function as well as oncogenesis. They are also working to infer novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships.
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Assistant Professor, Biomedical Informatics
Jing Chen, PhD, is a bioinformatician with more than 10 years’ experience in computational biology. He is currently researching methods to combine molecular, clinical, and phenotypic data to predict genes and pathogenic variants as they predispose individuals to particular pediatric diseases including childhood cancer, preterm birth and genetic diseases.
Assistant Professor, Biomedical Informatics.
Professor Harnett serves as the Director for the UC Center for Health Informatics (CHI) within the Department of Biomedical Informatics. The CHI is the institutional Honest Broker that provides clinical data for research. Other service lines include developing enterprise-class tools for organizing, displaying and visualizing data using analytics. His teaching and research includes medical informatics, patient-centered applications and telehealth. Brett also serves on the UC Institutional Review Board (IRB).
PubMed | ResearchGate
Anil Jegga, DVM, MRes, is a biological and medically-oriented computational biologist. The mission of the Jegga Lab is to design, develop and apply novel and robust computational approaches that will accelerate the diffusion of genomics into biomedical research and education and convert the genomics data deluge into systematized knowledge to help us understand the molecular basis of disease. The lab continues with their focus on integration and mining of multiple sources of genomic, genetic and biomedical data to derive models for pathways and processes underlying development, disease and drug response. Independently and collaboratively, they have previously developed and published tools that allow biologists with minimal computational experience to integrate diverse data types and synthesize hypotheses about gene and pathway function in human and mouse. These tools are designed to answer several straightforward questions that biologists frequently encounter while trying to apply systems-level analyses to specific biological problems. His team is currently focusing on developing and implementing systems biology-based novel computational approaches to identify drug candidates for rare lung disorders. They are also working to integrate and mine genomic and compound screening-based big data to identify drug repositioning and novel drug candidates.
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Assistant Professor, Biomedical Informatics.
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.
Google Scholar | ResearchGate
Hee Woong Lim, PhD, investigate transcriptional regulations of gene expression in various context including but not limited to metabolism, development, pathogenesis, and pharmacogenomics. He specifically focuses on enhancer regulations and tries to explain their regulatory mechanisms. To this end, he integrates various levels of genomic, epigenomic, and transcriptomic information from high-throughput data (GRO-seq, RNA-seq, ChIP-seq, ChIP-exo, etc) to understand detailed enhancer architectures and their distinct functions.
Associate Professor, Biomedical Informatics.
Long (Jason) Lu, PhD, focuses on bringing quantitative approaches from disciplines such as computer science and applied mathematics to study the molecular mechanisms of human diseases. His expertise includes biomolecular network predictions and analysis, machine learning and statistical inference, genomic and transcriptomic sequence analysis, and medical image analysis. Dr. Lu developed a network-based approach that combines proteomics experiments and computational predictions to discover high-density lipoprotein (HDL) subspecies and correlate them with cardiovascular protection function. His approach identified 38 candidate HDL subparticles. Further biochemical characterization of these putative subspecies may facilitate the mechanistic research of cardiovascular disease and guide targeted therapeutics aimed at its mitigation. In studying pediatric brain disorders, Dr. Lu developed a set of novel algorithms for analyzing brain anatomical and functional MRI images. These algorithms will be important tools in aiding physicians in diagnosis and developing treatment plans. Dr. Lu has also introduced a new perspective to characterize gene essentiality from protein domains, which addresses the limitations of traditional gene-level studies of essentiality. To identify such essential domains, he developed an Essential Domain Prediction (EDP) Model and presented the first systematic analysis on gene essentiality on the level of domains. Dr. Lu’s research accomplishments have been recognized nationally and internationally by serving on grant reviewer panels for the National Institutes of Health and the National Science Foundation in the United States, the Natural Sciences and Engineering Research Council of Canada (NSERC), French National Research Agency (ANR), and National Science Centre of Poland (NCN).
Publications List | Lab Website
Dr. Pestian's lab focuses on developing advanced technology for the care of neuropsychiatric illness. Using artificial intelligence, his team integrates analyses of trait and state characteristics for early identification of both neurological and psychiatric illness. His lab developed and implemented an automated, electronic health record surveillance system that processes clinician notes to identify epilepsy surgery candidates up to two years earlier than traditional approaches. The lab also focuses on earlier identification of individuals at risk of suicide, depression, and bipolar and anxiety disorders using verbal and non-verbal language. Current projects include fusion of linguistic, acoustic, and visual cues that are being tested in selected Cincinnati Public Schools and Cincinnati Children’s clinics. Dr. Pestian and his lab have 18 issued patents and he is active in the entrepreneurial community. This activity has yielded over 500 jobs and one-half billion in revenue have been created. One invention, Processing Text With Domain-Specific Spreading Activation Methods, is a platform for neuropsychiatric research. Another, Optimization and Individualization of Medication Selection and Dosing, has been used for optimal mental health drug selection on more than 420,000 people. He and his colleagues have published more than 80 peer reviewed publications that focus on applied and translational sciences apropos to artificial intelligence. He currently mentors five junior faculty, of which three have recently received funding from the National Institutes of Health (NIH). Dr. Pestian is an alumni of the NIH’s standing Study Section, Biomedical Library and Informatics Review Committee (BLIRC) of the National Library of Medicine,as well as the National Institute for Mental Health’s, Pathway to Independence (K99) study section.
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Assistant Professor, Biomedical Informatics.
Surya Prasath, PhD, is a mathematician with expertise in the application areas of image processing and computer vision. He received his PhD in mathematics from the Indian Institute of Technology Madras, India in 2009 (defended in March 2010). He has been a postdoctoral fellow at the Department of Mathematics, University of Coimbra, Portugal, for two years. From 2012 to 2017 he was with the Computational Imaging and VisAnalysis (CIVA) Lab at the University of Missouri, USA and worked on various mathematical image processing and computer vision problems. He had summer fellowships/visits at Kitware Inc. NY, USA, The Fields Institute, Canada, and IPAM, University of California Los Angeles (UCLA), USA. His main research interests include nonlinear PDEs, regularization methods, inverse and ill-posed problems, variational, PDE based image processing, and computer vision with applications in remote sensing, biomedical imaging domains.
With the sequencing of human genome nearing completion, there was a lot of interest in applying comparative genomics approaches to help unlock the secrets of the human genome. Dr. Roskin worked with David Haussler as part of the Mouse Genome Sequencing Consortium. Their goal was to analyze the genomic differences between mammalian species to find regions of the human genome that are conserved not by chance but because of evolutionary constraint. To that pairwise analysis, Dr. Roskin added data from the Rat Genome Sequencing Consortium to allow triangulation of evolutionary events across the whole genome. A major result in the Mouse genome paper was Dr. Roskin's estimate of the share of the human genome under purifying selection. He developed score functions to detect unusually conserved regions in the human genome by comparing it to the mouse. By looking at the distribution of conservation scores genome-wide compared to the scores of regions under neutral section, he estimated that 5% of human genome is under purifying selection. This percentage is much higher than the fraction of the genome that codes for proteins and spurred new interest in function non-coding elements of the human genome.
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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.
Lab Website | Google Scholar | Research Gate
Assistant Professor, Biomedical Informatics.
Dr. Sarangdhar is a bioinformatics-trained computational scientist interested in unraveling the underlying causes and mechanisms of drug toxicity. His research focuses on integrating high-dimensional computational approaches with systems biology knowledgebases to accelerate the discovery of novel drug-toxicity relationships buried in heterogeneous big data. Dr. Sarangdhar developed a novel platform, AERSMine, to mine the clinical responses of millions of patients to all FDA-approved drugs in order to identify unexpected clinical harm, benefits and alternative treatment choices for individual patients. AERSMine provides an insight into sub-population-specific differential therapeutic risks, and creates an avenue to improve our understanding of the molecular basis of adverse drug reactions. Dr. Sarangdhar is also a member of the Children’s Oncology Group and is leading the effort to delineate differential treatment- and age-specific toxicity profiles within pediatric and young adult cancer patients across multiple studies and disease groups. He is designing computational approaches that facilitate effective analysis of large-scale datasets including clinical trials so we can identify a) the true regimen-specific differential risks associated with chemotherapy, b) the underlying genetic factors that drive exacerbation of toxicities, and c) recognize effective personalized therapeutic strategies for individuals at highest risk of complications. His group is developing integrative analytical approaches that combine machine learning techniques with toxicity data, genotype-phenotype relationships, and gene-regulatory mechanisms, to help facilitate modelling novel and effective therapeutics.
Google Scholar | Research Gate
Professor, Biomedical Informatics and Chief Medical Information Officer.
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.
Dr. Wagner has a long-standing interest in applications of machine learning techniques to bioinformatics problems such as protein structure prediction, disease classification and protein identification. He is also involved in a number of projects that implement complex software and data infrastructure. For the National Heart Lung and Blood Institute-funded Pediatric Cardiology Genomics Consortium, part of the Bench to Bassinet project, he plays a leadership role in the development and maintenance of the Data Hub (a.k.a. HeartsMart), which now houses tens of thousands of whole exome and thousands of whole genome sequencing data sets. He is co-principal investigator on the Longitudinal Pediatric Data Resource (LPDR) project funded through the Newborn Screening Translational Research Network and National Institute of Child Health and Human Development. The LPDR is being used by researchers nationwide to mine health outcome data over the lifespan of children who screen positive for rare and often devastating genetic disorders. Dr. Wagner also leads the Rheumatology Disease Research Informatics Core of the Cincinnati Rheumatic Diseases Core Center, which is funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases.
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Assistant Professor, Biomedical Informatics
Danny T.Y. Wu recently joined the University of Cincinnati Department of Biomedical Informatics (BMI) as an Assistant Professor. His research draws on human-computer interaction, data mining, information retrieval, and natural language processing to maximize the value of clinical data stored in electronic health records to improve care quality and support clinical and translational research. Wu received both his PhD and master’s degree from the University of Michigan School of Information prior to joining UC. Before going to graduate school, he worked as a software engineer for four years. In addition to research, Wu is dedicated to education, service, and practical engagement. He was appointed as a Lecturer in the Department of Health Management and Policy at the University of Michigan in Fall 2015, teaching a graduate-level course on database systems and Internet applications. He was a senior analyst leading a programming team to develop, implement, and innovate dynamic data capture systems at the University of Michigan Congenital Heart Center. Wu served on the student editorial board of the Journal of American Medical Informatics Association in 2015 and 2016.
Assistant Professor, Emergency Medicine
Dr. Dexheimer has a background in developing, implementing and evaluating clinical information systems including clinical decision systems, organizational and workflow aspects of informatics applications, computerized applications for emergency medicine and implementation of artificial intelligence techniques, computerized guideline applications and evidence-based medicine, public health informatics, and preventive care measures. Her research focuses on the design, implementation and evaluation of clinical decision support systems in pediatric emergency medicine to improve clinical care.
ResearchGate | PubMed
Attending Physician and Assistant Professor, Division of Critical Care Medicine
The focus of Dr. Dewan’s research is clinical decision support with a specific focus on using implementation science to improve the outcomes for high risk pediatric patients. She is currently working to develop a clinical decision support identification tool for patients in the pediatric intensive care unit at high risk of cardiac arrest. Other work includes clinical decision support around sepsis identification and mitigation, alarm fatigue reduction, and improving healthcare value.
Attending Neonatologist, Assistant Professor, Neonatology & Pulmonary Biology
Kevin Dufendach, MD, MS is an Assistant Professor the divisions of Neonatology and Pulmonary Biology and Biomedical Informatics. His research focuses on user-centered design of electronic health record system software. Specifically, he is interested in incorporating human factors principles into the design of human-computer interfaces to better improve information communication for both provider-facing as well as patient-facing applications. Dr. Dufendach’s current research seeks to improve parental engagement in the neonatal intensive care unit through a neonatal-specific inpatient portal application.
Co-Medical Director, Division of Hospital Medicine, Liberty Campus and Assistant Professor
Dr. Hagedorn is a pediatric clinician and researcher currently practicing in the Division of Hospital Medicine at Cincinnati Children's Hospital Medical Center. His interests include improving the care and safety of children through optimization of Electronic Health Record (EHR) decision support tools, developing data and development pipelines to enable frontline quality improvement work through analytics and visualization. Past work from Dr. Hagedorn includes examining opportunities for implementing clinical decision rules in the EHR and using analytics and visualization to target improvement of medication alerts. Present and future work involves continued evolution of analytics and visualization techniques to gain insight into operational and clinical data, and leveraging clinical data sources to enable more nimble quality improvement work through visualization, notification and deeper insight into successes and failures.
Professor, Center for Autoimmune Genomics – CAGE
A large portion of Dr. Kaufman's research career has been on the genetics of systemic lupus erythematsus. Their work has screened 10's of thousands of lupus cases and controls with millions of polymorphic markers. This work has resulted in the identification, replication and/or fine mapping of over 70 genetic associations with systemic lupus erythematsus. Recently, they have taken advantage of next generation DNA sequencing to identify variants that directly cause disease. They have developed a number of bioinformatic pipelines that can be applied to any phenotype. These automated pipelines are part of the Cincinnati Analytical Suite for Sequencing Informatics (CASSI) which has been applied to more than 20 different diseases and provides a list of candidate causative variants that lead to disease.
Assistant Professor, Experimental Hematology & Cancer Biology
Komurov lab focuses on the systems biology of cancer. We develop and employ computational data mining tools to interrogate clinically exploitable cancer mechanisms from cancer genomics data, and use experimental approaches in vitro and in animal models for their molecular characterization. Specifically, we are studying the core aberrations in the genomic, RNA and protein homeostasis networks in cancers, their role in cancer pathogenicity and therapy response, and the synthetic vulnerabilities imposed by these defects on the tumor cell. In addition, we are developing computational methods and software to enable intuitive and effective functional mining of genomic data.
Lab Website | PubMed
Professor, Environmental Health
Dr. Medvedovic is developing and applying new statistical and computational methods for the analysis of “big data” in the context of biomedical research. His recent work is focused on the reconstruction of regulatory networks using libraries of genome-scale signatures of cellular perturbations. He is also developing protocols for analyzing next-generation sequencing data, and working on development and application of unsupervised statistical learning approaches based on the non-parametric Bayesian models. He is also the director of the Division of Biostatistics and Bioinformatics in UC's Department of Environment Health.
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Graduate Program Director, Associate Professor, Environmental Health
Dr. Meller serves Graduate Program Director for Biomedical Informatics and also pursues several lines of research in molecular modeling, structural bioinformatics and computational genomics, at the intersection of data science and biomedicine. Dr. Meller and his group have developed a number of successful methods for the prediction of protein structure, protein-protein interactions and functional hot spots in proteins. Several web servers developed by the group, including Sable, Sppider, Minnou and Polyview have widely been used, with a total of over 1 million submissions from more than 30,000 users in many countries. Dr. Meller has also been active in the development and applications of methods for knowledge extraction from high dimensional genomic data. He and his group have been involved in many collaborative projects with direct medical relevance. Examples include identification of markers associated with disease subtypes in cancer and autoimmunity, modeling of signal transduction pathways in differentiation and development, and developing inhibitors of critical protein-protein interactions in autophagy, bone marrow transplants, and pathogen-host interactions.
Lab Website | Google Scholar | PubMed
Assistant Professor, Immunobiology
The focus of Dr. Miraldi’s research is development of computational methods to build predictive, mathematical models of the immune system from high-dimensional genomics measurements. She collaborates with experimental immunologists to learn how diverse immune cells sense and respond to their environment in both physiological and disease contexts. Miraldi designs studies and develops methods that leverage breakthroughs in biotechnologies, including chromatin accessibility and single-cell gene expression measurements. The resulting genome-scale models (e.g., of transcriptional regulation) provide unbiased, experimentally testable hypotheses. Miraldi’s long-term goal is to use these models to specifically re-engineer immune-cell behavior in the context of autoimmune and other diseases.
Associate Professor, Center for Autoimmune Genomics and Etiology - CAGE
Dr. Porollo is a computational biologist with research focused on the development of new prediction and analytical methods in structural bioinformatics. Applications of computational approaches include structural and functional characterization of proteins and their mutations, rational protein engineering, analysis of biological pathways, identification of new drug targets, virtual drug screening, and microbiome analysis.
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Associate Chief, Department of Radiology, Associate Professor, Radiology and Medical Imaging
Dr. Towbin is a radiologist, the Neil D. Johnson Chair of Radiology Informatics, and Associate Chief of Radiology, Clinical Operations and Radiology Informatics at Cincinnati Children's. He is a recognized leader in enterprise imaging, structured reporting, and workflow efficiency. In his clinical role, Dr. Towbin specializes in abdominal imaging. His research focuses on radiology clinical informatics, quality improvement, cancer imaging, and imaging of the liver.
PubMed | ResearchGate
Associate Professor, Center for Autoimmune Genomics & Etiology - CAGE
Dr. Weirauch is a computational biologist. His lab seeks to understand the mechanisms of gene transcriptional regulation. Current projects focus on characterizing transcription factor binding specificities, and developing methods for modeling their interactions with DNA, both in vitro and in vivo. His lab applies insights from basic research on transcription factor-DNA interactions to study the mechanisms underlying complex diseases.
Lab Website | Google Scholar | ResearchGate
Associate Professor, Department of Pediatrics
Dr. Xu’s research interest is to develop and apply bioinformatics and systems biology approaches to gain a better understanding of molecular mechanisms behind big data sets. Her current lines of research are focusing on the identification of gene signatures, regulatory networks, and biological pathways controlling lung maturation and diseases. She is actively involved in using high-throughput single cell genomics for the development of LungMAP, a web-based data resource funded by the National Heart, Lung and Blood Institute to provide useful tools and resources for the lung research community.
Assistant Professor, Electrical Engineering and Computer Systems.
The focus of Dr.Alturi's research is to develop novel data science insights and methodologies that will accelerate the pace of scientific discovery. Specifically, his main thrust is in developing techniques for discovering untapped information in space-time data that is becoming ubiquitous in several domains, including neuroscience, climate science, mobile health, and social sciences. Development of novel frameworks for knowledge discovery is crucial to tackle the challenges introduced by the characteristics of the data and the new data science problems that arise in these domains. Some key directions in his research that are motivated from the above disciplines include studying networks in space-time data, comparing space-time instances, discovering patterns, and integrating data from different sources. With his work in these directions, he hopes to advance data science and have a far reaching impact in the form of scientific discoveries in several application domains.
Lab Website | Publications
Assistant Professor, UC Department of Pediatrics.
Dr. Barski is interested in epigenomics and transcriptional regulation of gene expression. His PhD at the University of Southern California was focused on transcriptional regulation in osteoblasts. During his post-doctoral training in Keji Zhao lab at NIH, Dr. Barski took part in the development of ChIP-Seq, a revolutionary method that combines ChIP with the next-generation sequencing. ChIP-Seq allows genome-wide mapping of chromatin modifications and transcription factor binding sites with resolution and sensitivity far exceeding older methods. Since his arrival to Cincinnati Children’s Hospital Medical Center in 2011, Dr. Barski is working on epigenomics of immunological memory. His laboratory is also developing both wet lab and computational approaches to the study of epigenomics and runs Cincinnati Children's Epigenomics Data Analysis Core. Dr. Barski has publications in prestigious journals including Cell, Nature Structural and Molecular Biology Genome Biology and others. He is a recipient of NHLBI Career Transition Award (K22) and NIH Director’s New Innovator Award (DP2). See his BioWardrobe
Lab Website | PubMed
Professor, Electrical Engineering and Computer Systems.
The main focus of Dr. Bhatnagar's research has been on data mining and pattern recognition problems. More recent studies have developed data mining algorithms for very large and distributed database problems and have applied these algorithms to many application domains including the bioinformatics area. Recent projects include subspace clustering and formal concept analysis for very large datasets, which seeks to develop efficient algorithms on hadoop, using map-reduce paradigm, for mining multi-domain subspace clusters from multiple datasets; mutual K-means clustering algorithms for density-based clusters, and content-based retrieval of images from large spatio-temporal image databases.
Lab Website | Publications
Professor, Internal Medicine.
Dr. Eckman is a general internist and decision scientist. His research interests lie in combining both clinical and theoretic applications of decision analysis to the care of individual patients and to broader issues of health policy. In particular his methodological interests have included the development of patient-specific decision support tools, cost-effectiveness analysis, and the continued study and development of new decision analytic methods. He uses quantitative methods to help make decisions about the allocation of increasingly scarce health care resources. He also has a long-standing interest in decision analytic issues surrounding anticoagulation therapy within a variety of clinical situations, including atrial fibrillation, venous thromboembolism, and thrombophilic states.
Google Scholar | ResearchGate | PubMed
Associate Professor, Asthma Research.
Dr. Mersha's research combines quantitative, ancestry and statistical genomics approaches to unravel genetic and non-genetic contributions to complex diseases and racial disparities in human population, particularly asthma and asthma-related allergic disorders. Current research in his laboratory include: 1) admixture and association analysis; 2)transcriptome profiling studies; 3) microbiome/epigenome analysis, and 4) developing web-based bioinformatics tools specifically designed to integrate omics from public databases (e.g., 1000 Genomes Project, ENCODE and Epigenome Roadmap). Dr. Mersha's long-term career goals are to develop a program that will lead to an in-depth understanding of the complex interplay between genomic variations and environmental exposure risk factors in the etiology of complex diseases, including asthma.
Lab Website | PubMed
Professor, Environmental Health and Biomedical Engineering
Dr. Rao’s research interests include Biostatistics; Statistical Genetics; Survival Analysis; Internet Health Data; Data Mining; Machine Learning; Tissue Engineering; Medical Imaging; and Data Science.
Associate Professor, Internal Medicine.
Dr. Schauer has expertise in the decision sciences, patient-centered outcomes and comparative effectiveness research. Much of his current research is focused on obesity and outcomes associated with bariatric surgery. He is the Principal Investigator on a grant funded by the National Cancer Institute that is examining the relationship between obesity, cancer and intentional weight loss. He has experience using many of the large publicly available datasets including the National Health Interview Survey that is linked to the National Death Index and the Nationwide Inpatient Sample in his research. He has also collaborated with the HMO Research Network using their data sources. Additionally, as associate program director for resident research, he oversees all of the resident research in the Department of Internal Medicine.
Publications | ResearchGate
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