The University of Michigan Bioinformatics Training Program Abstract This is a proposal to renew and grow the University of Michigan Bioinformatics Training Program (U-M BITP), now in year 10, to 8 trainee slots/year for 2-3 years of pre-doctoral training, usually between years 1 and 3. BITP continues to be a part of our now well established and widely respected U-M Bioinformatics Graduate Program (BGP). The BGP (and its BITP) is an interdisciplinary graduate training program in bioinformatics and computational biology, drawing faculty from the School of Medicine, College of Engineering, College of Literature, Sciences and the Arts (LS&A; Including the Departments of Mathematics, Statistics, Chemistry, and Physics), the School of Public Health, School of Nursing, the College of Pharmacy, and the School of Information. The BGP and BITP are embedded in the U-M Center for Computational Medicine and Bioinformatics (CCMB), an interdisciplinary research and education center that provide the interdisciplinary research and training context. CCMB currently has 127 affiliate faculty members across the U-M, 48 of whom are participating in this BITP as potential primary mentors. CCMB is hosted within our University of Michigan Medical School Department of Computational Medicine and Bioinformatics (DCM&B), which currently has 14 primary faculty appointments, plus 12 affiliate faculty, and 4 research track core faculty members. All core DCM&B faculty members are eligible mentors of BITP trainees. BITP trainees have a full curriculum of Bioinformatics, Statistics, Data Science, and Biology/Biomedicine graduate courses to choose from, journal clubs, seminars, workshops, and special events. The BITP dissertation training utilizes a ?dual-mentor? approach, which combines quantitative/computational and DBP application elements. The U-M-based tranSMART Foundation, cancer biostatistics, proteome informatics activities, NIDDK Metabolomics Center, and the CTSA Biomedical Informatics Program have all achieved national recognition, and are a natural magnet for BITP trainees. The goal of the BITP is to train students in bioinformatics and applied computational biology by engaging them in a rigorous curriculum and pre-doctoral training experience. BITP trainees engage in cutting-edge collaborative research featuring a strong ?driving biological problem? (DBP) application, often leading BTP trainees to have a strong T1 Translational Research orientation. The BGP has graduated 55 Ph.D. trainees in bioinformatics since its first in 2006. Within this trainee cohort, 10 BTP trainees have graduated to date. These graduates have launched exciting careers in industry, academics, and government. The BGP and BITP has established a Data Science Training Track, and is actively engaged in the emerging Michigan Institute for Data Science (MIDAS), which will be offering a Data Science Certificate to enhance bioinformatics training for Data Science Concentrators. The overall objective of the BTP is to provide the finest Bioinformatics Training environment and trainee experience available in the US.
This is a proposal to renew and grow the University of Michigan Bioinformatics Training Program (U-M BITP) to 8 trainee slots/year for 2-3 years of Pre-doctoral Training in Bioinformatics, usually between years Graduate Student years 1-3. BITP continues to be an integral part of our now well established and highly regarded U-M Bioinformatics Graduate Program (BGP), entering its 15th year.
|Duda, Marlena; Zhang, Hongjiu; Li, Hong-Dong et al. (2018) Brain-specific functional relationship networks inform autism spectrum disorder gene prediction. Transl Psychiatry 8:56|
|Kalinin, Alexandr A; Allyn-Feuer, Ari; Ade, Alex et al. (2018) 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification. Sci Rep 8:13658|
|Ansari, Sardar; Farzaneh, Negar; Duda, Marlena et al. (2017) A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records. IEEE Rev Biomed Eng 10:264-298|
|Gurdziel, Katherine; Vogt, Kyle R; Walton, Katherine D et al. (2016) Transcriptome of the inner circular smooth muscle of the developing mouse intestine: Evidence for regulation of visceral smooth muscle genes by the hedgehog target gene, cJun. Dev Dyn 245:614-26|
|Moyers, Bryan A; Zhang, Jianzhi (2016) Evaluating Phylostratigraphic Evidence for Widespread De Novo Gene Birth in Genome Evolution. Mol Biol Evol 33:1245-56|
|Cavalcante, Raymond G; Patil, Snehal; Weymouth, Terry E et al. (2016) ConceptMetab: exploring relationships among metabolite sets to identify links among biomedical concepts. Bioinformatics 32:1536-43|
|Nemzek, Jean A; Hodges, Andrew P; He, Yongqun (2015) Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury. BMC Res Notes 8:516|
|Rolland, Delphine; Basrur, Venkatesha; Conlon, Kevin et al. (2014) Global phosphoproteomic profiling reveals distinct signatures in B-cell non-Hodgkin lymphomas. Am J Pathol 184:1331-42|
|Holmen, Oddgeir L; Zhang, He; Fan, Yanbo et al. (2014) Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nat Genet 46:345-51|
|Holmen, Oddgeir L; Zhang, He; Zhou, Wei et al. (2014) No large-effect low-frequency coding variation found for myocardial infarction. Hum Mol Genet 23:4721-8|
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