The Stanford Biomedical Informatics (BMI) training program offers MS and PhD degrees to students with an intensive training that prepares them for careers in research. The formal core curriculum consists of (1) Core BMI, (2) Computer Science, Statistics, Mathematics and Engineering, (3) Ethical, Social, Legal Implications of technology, and (4) Domain biology or medicine. The curriculum is appropriate for basic informatics training in Health care/clinical informatics (HC), Translational Bioinformatics (TB), and Clinical Research Informatics (CR). Stanford's research milieu provides outstanding opportunities for informatics research. We are in the 29th year as an NLM-supported training program, with a steady state of about 51 total students (32 PhD, 5 MS, 8 Co-terminal MS, and 6 Professional/distance MS). We have produced 160 graduates (PhD and MS), ~60% of which were NLM-supported at some period. In this proposal, we request continuing support for training 10 predoctoral (2-3 years of support), 5 postdoctoral candidates (2 years of support), and 4 short-term diversity trainees (1 quarter of support) per year, representing a shift towards more postdoctoral trainees and more short-term trainees. We receive more than 120 applicants per year for our PhD and MS program (a subset of which are NLM supported). We propose to continue our training program with an increase focus on training post-doctoral individuals, under-represented minorities and women. The BMI program will continue to produce leaders in academic and industrial biomedical informatics, and will continue to prepare them for an exciting future using biomedical information to advance human health.

Public Health Relevance

Stanford has a 30-year history of training in biomedical informatics. This proposal outlines a plan to support the next generation of leaders in biomedical informatics through a rigorous program of research training, involving core class work, deep research on important current problems, ethics, and skills in oral and written communication.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
5T15LM007033-30
Application #
8496873
Study Section
Special Emphasis Panel (ZLM1-AP-T (01))
Program Officer
Florance, Valerie
Project Start
1984-07-01
Project End
2017-06-30
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
30
Fiscal Year
2013
Total Cost
$845,501
Indirect Cost
$49,673
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Zhang, Weiruo; Bouchard, Gina; Yu, Alice et al. (2018) GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma. Cancer Res 78:3445-3457
Liu, Boxiang; Pjanic, Milos; Wang, Ting et al. (2018) Genetic Regulatory Mechanisms of Smooth Muscle Cells Map to Coronary Artery Disease Risk Loci. Am J Hum Genet 103:377-388
Schuler, Alejandro; Callahan, Alison; Jung, Kenneth et al. (2018) Performing an Informatics Consult: Methods and Challenges. J Am Coll Radiol 15:563-568
Banovich, Nicholas E; Li, Yang I; Raj, Anil et al. (2018) Impact of regulatory variation across human iPSCs and differentiated cells. Genome Res 28:122-131
Hollingsworth, Scott A; Dror, Ron O (2018) Molecular Dynamics Simulation for All. Neuron 99:1129-1143
Huang, Edmond Y; To, Milton; Tran, Erica et al. (2018) A VCP inhibitor substrate trapping approach (VISTA) enables proteomic profiling of endogenous ERAD substrates. Mol Biol Cell 29:1021-1030
Latorraca, Naomi R; Wang, Jason K; Bauer, Brian et al. (2018) Molecular mechanism of GPCR-mediated arrestin activation. Nature 557:452-456
Sweeney, Timothy E; Azad, Tej D; Donato, Michele et al. (2018) Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters. Crit Care Med 46:915-925
Koh, Andrew S; Miller, Erik L; Buenrostro, Jason D et al. (2018) Rapid chromatin repression by Aire provides precise control of immune tolerance. Nat Immunol 19:162-172
Marafino, Ben J; Dudley, R Adams; Shah, Nigam H et al. (2018) Accurate and interpretable intensive care risk adjustment for fused clinical data with generalized additive models. AMIA Jt Summits Transl Sci Proc 2017:166-175

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