Michael Wagner, PhD

Faculty Liaison, Biomedical Informatics Core

Academic Affiliations

Associate Professor, UC Department of Pediatrics

Phone 513-636-2935

Fax 513-636-2056

Email michael.wagner@cchmc.org


Large-scale optimization; applications in bioinformatics


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 the National Heart Lung and Blood Institute-funded Pediatric Cardiology Genomics Consortium, part of the Bench to Bassinet project, he plays a leadership role in the development and maintenance of the Data Hub (a.k.a. HeartsMart), which now houses tens of thousands of whole exome and thousands of whole genome sequencing data sets. He is co-principal investigator on the Longitudinal Pediatric Data Resource (LPDR) project funded through the Newborn Screening Translational Research Network and National Institute of Child Health and Human Development. The LPDR is being used by researchers nationwide to mine health outcome data over the lifespan of children who screen positive for rare and often devastating genetic disorders. Dr. Wagner also leads the Rheumatology Disease Research Informatics Core of the Cincinnati Rheumatic Diseases Core Center, which is funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases.

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

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

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

View PubMed Publications

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

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

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

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

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