NIH Graduate Training Program
The Predoctoral Training Program (T32) in Biological Data Science is funded by the National Institutes of Health/NIGMS award T32GM128596
NIH Graduate Training Program
The Predoctoral Training Program (T32) in Biological Data Science is funded by the National Institutes of Health/NIGMS award T32GM128596
Overview
Due to genomic technologies, electronic medical records, and digitized high-throughput experiment readouts, building an independent biomedical research career requires fluency in data science. While this challenge is often addressed with basic skill building for biomedical doctoral students, there is less support for biomedical trainees with significant skills in computational and data sciences. These doctoral students doing data-driven biomedical research may be siloed, are rarely trained to critique quantitative approaches, and often develop computational methods that are difficult for their thesis lab to maintain and update. Data science curricula and workshops are often stewarded by computer scientists and statisticians and rarely focus on biological data or feedback from biological models to methods development. There is an urgent need to develop centralized interdisciplinary training focused on fostering Biological Data Scientists: scientific dual citizens, whose research is motivated by biological systems and who develop quantitative methods for analyzing large-scale datasets. The established Biological Data Science training community at Brown University is maintained by 29 engaged faculty mentors across multiple disciplines who jointly and actively mentor our NIH-supported predoctoral trainees each year. Our activities include: graduate seminars focused on extensive peer review of methods for analyzing biological data, a program retreat featuring data science workshops developed by trainees with an emphasis on rigor and reproducibility, a near-peer mentoring network, and a series of professional development events for interdisciplinary researchers.
Recent Publications & Achievements
- Dalgarno, The Functional Impact of Nuclear Reorganization in Cellular Senescence (Neretti Lab)
- Babcock, Adult Hippocampal Neurogenesis in Aging and Alzheimer's Disease (Webb Lab)
- Banuelos, The History and Evolution of the Denisovian-EPAS1 Haplotype in Tibetans (Huerta-Sanchez Lab)
- Banuelos, Our Tangled Family Tree: New Genomic Methods Offer Insight into the Legacy of Archaic Admixture
- Smith, Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics (Ramachandran Lab)
- Penumutchu, Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome (Belenky Lab)
- Penumutchu, Microbial Competition between Escherichia coli and Candida albicans Reveals a Soluble Fungicidal Factor (Belenky Lab)
In Press
- Smith, Enrichment Analyses Identify Shared Associations for 25 Quantitative Traits in Over 600,000 Individuals from 7 Diverse Ancestries (Crawford Lab, Ramachandran Lab)
- Banuelos, Associations Between Forensic Loci and Neighboring Gene Expression Levels May Compromise Medical Privacy (Huerta-Sanchez Lab)
- Congratulations to Leah Darwin in the Rand Lab and Melanie Ortiz Alvarez de la Campa in the Belenky Lab on their 2023 NSF Graduate Research Fellowship Program (GRFP) awards.
- Congratulations Sam Smith, our recent Comp Bio doctoral grad, on being named one of two winners of the CW Cotterman Award from the American Journal of Human Genetics for 2022.
- Congratulations to Mayra Banuelos in the Huerta-Sanchez Lab and Cole Williams in the Ramachandran Lab, as well as our honorable mention recipients David Peede and Vivek Ramanan on their 2022 NSF Graduate Research Fellowship Program (GRFP) awards.
Program Directors
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Sohini Ramachandran
Director of the Data Science Institute, Hermon C. Bumpus Professor of Biology and Data Science, Professor of Computer Science -
Bjorn Sandstede
Alumni-Alumnae University Professor of Applied Mathematics, Chair of Applied Mathematics -
Eliezer Upfal
Rush C. Hawkins University Professor of Computer Science -
Zhijin Wu
Director of the Doctoral Program in Biostatistics, Professor of Biostatistics
About the Ph.D. program in Computational Biology