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.
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.
| 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 |
Showing the most recent 10 out of 262 publications
