The Stanford Biomedical Informatics (BMI) training program continues to offer MS and PhD degrees to students with an intensive training that prepares them for careers in research. The formal core curriculum trains them in a five part curriculum that features (1) core biomedical informatics, (2) domain biology or medicine (3) computer science, (4) probability and statistics, and (5) ethical, legal, and social issues. The curriculum offers general training in biomedical informatics, appropriate for students whose research subsequently focuses on diverse application areas within biomedicine. The program has particular strength in bioinformatics and clinical translational research. However, Stanford's research milieu provides excellent opportunities for basic informatics research in other areas as well. We are in the 22nd year as an NLM supported training program, with a steady state of about 32 total students. We have produced 102 graduates, 55 of whom were NLM-supported at some period. In this proposal, we request continuing support for the training of fourteen pre-doctoral and four post-doctoral candidates per year, representing a small shift in balance in response to our applicant pool. Most training slots will be used for BMI degree candidates, although we will take advantage of program flexibility to fund a small number of post-MD or Ph.D. candidates who are not BMI degree candidates. We are currently receiving more than 80 applications for 4-8 total spots in our program (a subset of which are NLM supported). We propose to continue our successful training program in the next five years, with an expanded executive committee, increased recruitment of participating faculty, a consulting service to allow students to have direct contact with biomedical researchers, and a continuing plan for increased affirmative action recruitment of women and minority students. The BMI program will therefore continue to produce leaders in academic and industrial biomedical informatics, and will continue to respond to the changing landscape of biomedical research in the information age.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
5T15LM007033-28
Application #
8090337
Study Section
Special Emphasis Panel (ZLM1-AP-T (O1))
Program Officer
Florance, Valerie
Project Start
1984-07-01
Project End
2012-06-30
Budget Start
2011-07-01
Budget End
2012-06-30
Support Year
28
Fiscal Year
2011
Total Cost
$939,534
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Haynes, Winston A; Tomczak, Aurelie; Khatri, Purvesh (2018) Gene annotation bias impedes biomedical research. Sci Rep 8:1362
Sylman, Joanna L; Boyce, Hunter B; Mitrugno, Annachiara et al. (2018) A Temporal Examination of Platelet Counts as a Predictor of Prognosis in Lung, Prostate, and Colon Cancer Patients. Sci Rep 8:6564
Powers, Scott; Qian, Junyang; Jung, Kenneth et al. (2018) Some methods for heterogeneous treatment effect estimation in high dimensions. Stat Med 37:1767-1787
Lam, Carson; Yu, Caroline; Huang, Laura et al. (2018) Retinal Lesion Detection With Deep Learning Using Image Patches. Invest Ophthalmol Vis Sci 59:590-596
Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E et al. (2018) Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome. Pac Symp Biocomput 23:56-67
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

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