A photo of Michael Wagner.

Michael Wagner, PhD

  • Associate Professor, UC Department of Pediatrics
  • UC Department of Biomedical Informatics



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.


Large-scale optimization; applications in bioinformatics

Research Areas

Biomedical Informatics



Cxcr2 signaling and the microbiome suppress inflammation, bile duct injury, and the phenotype of experimental biliary atresia. Jee, J; Mourya, R; Shivakumar, P; Fei, L; Wagner, M; Bezerra, JA. PloS one. 2017; 12:e0182089.


SLICE: determining cell differentiation and lineage based on single cell entropy. Guo, M; Bao, EL; Wagner, M; Whitsett, JA; Xu, Y. Nucleic Acids Research. 2017; 45:e54.


Noise Exposure on Pediatric Inpatient Units. Daraiseh, NM; Hoying, CL; Vidonish, WP; Lin, L; Wagner, M. Journal of Nursing Administration. 2016; 46:468-476.


Genetic risk signatures of opioid-induced respiratory depression following pediatric tonsillectomy. Biesiada, J; Chidambaran, V; Wagner, M; Zhang, X; Martin, LJ; Meller, J; Sadhasivam, S. Pharmacogenomics. 2014; 15:1749-1762.


Early microbial and metabolomic signatures predict later onset of necrotizing enterocolitis in preterm infants. Morrow, AL; Lagomarcino, AJ; Schibler, KR; Taft, DH; Yu, Z; Wang, B; Altaye, M; Wagner, M; Gevers, D; Ward, DV; Kennedy, MA; Huttenhower, C; Newburg, DS. Microbiome. 2013; 1:13.


Solvent and lipid accessibility prediction as a basis for model quality assessment in soluble and membrane proteins. Phatak, M; Adamczak, R; Cao, B; Wagner, M; Meller, J. Current Protein and Peptide Science. 2011; 12:563-573.


Kolmogorov-Smirnov scores and intrinsic mass tolerances for peptide mass fingerprinting. Jain, R; Wagner, M. Journal of Proteome Research. 2010; 9:737-742.


Linear regression models for solvent accessibility prediction in proteins. Wagner, M; Adamczak, R; Porollo, A; Meller, J. Journal of Computational Biology. 2005; 12:355-369.


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

Maternal regulation of biliary disease in neonates via gut microbial metabolites. Jee, JJ; Yang, L; Shivakumar, P; Xu, PP; Mourya, R; Thanekar, U; Yu, P; Zhu, Y; Pan, Y; Wang, H; Luo, Z; Ollberding, NJ; Tang, ST; Bezerra, JA. Nature Communications. 2022; 13:18.