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
- Congratulations to David Peede in the Huerta-Sanchez Lab who was selected as a 2024 Blavatnik Family Fellow by Brown University.
- Congratulations to Elsie Chevy who gave a talk titled Population Genetics Meets Ecology: A Guide to Simulations in Continuous Geography at the 2024 Allied Genetics Conference (TAGC).
- Congratulations to Melanie Ortiz Alvarez de la Campa who was recently awarded First Place Poster Presentation at the 2024 Mind Brain Research Day at Brown University.
- Congratulations to David Peede in the Huerta-Sanchez Lab who was awarded the 2024 Brown University Presidential Award for Excellence in Teaching.
- Congratulations to Hannah Hoff in the Kartzinel Lab for being awarded the 2024 James Reveal Eriogonum Project Grant from the Eriogonum Society.
- Congratulations to Mayra Banuelos in the Huerta-Sanchez Lab on being named a 2023 Howard Hughes Medical Institute (HHMI) Gilliam Fellow.
- 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 to Chibuikem Nwizu in the Crawford Lab who was awarded the inaugural Black in Genetics Fellowship in 2023.
- Congratulations to Kelsey Babcock in the Webb Lab and Fleischmann Lab on being selected as a 2023 Blavatnik Family Fellow at Brown University.
- 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.
- Congratulations to Swathi Penumutchu in the Belenky Lab on being awarded the 2022 Charles "Chick" Kuhn Graduate Award.
- Ortiz-Alvarez de la Campa, Gut Biogeography Accentuates Sex-Related Differences in Murine Microbiome (Belenky Lab, 2024)
- Ramanan, Characteristic Attribute Organization System (CAOS): Identifying Classification Rules Based on Phylogenetically Organized Sequences (Sarkar Lab, 2024)
- Ramanan, Augmenting Bacterial Similarity Measures using a Graph-Based Genome Representation (Sarkar Lab, 2024)
- Hinthorn, Heterogeneity of Increased Biological Age in Type 2 Diabetes Correlates with Differential Tissue DNA Methylation, Biological Variables, and Pharmacological Treatments (Neretti Lab, 2024)
- Peede, Leveraging Shared Ancestral Variation to Detect Local Introgression (Huerta-Sanchez Lab, 2024)
- Hoff, Body Size Modulates the Extent of Seasonal Diet Switching by Large Mammalian Herbivores in Yellowstone National Park (Kartzinel Lab, 2024)
- Darwin, Sex, Tissue, and Mitochondrial Interactions Modify the Transcriptional Response to Rapamycin in Drosophila (Rand Lab, 2024)
- Bigness, scNODE: Generative Model for Temporal Single Cell Transcriptomic Data Prediction (Singh Lab and Larschan Lab, 2024)
- Gibson, Integrating Sex-Bias into Studies of Archaic Introgression on Chromosome X (Ramachandran Lab, 2023)
- Hinthorn, Single-Cell Transcriptomics of Peripheral Blood in the Aging Mouse (Neretti Lab, 2023)
- Ortiz-Alvarez de la Campa, Building a Queer- and Trans-Inclusive Microbiology Conference (Belenky Lab, 2023)
- Dalgarno, The Functional Impact of Nuclear Reorganization in Cellular Senescence (Neretti Lab, 2022)
- Golovanevsky, Multimodal Attention-Based Deep Learning for Alzheimer's Disease Diagnosis (Eickhoff Lab and Singh Lab, 2022)
- Banuelos, Associations Between Forensic Loci and Expression Levels of Neighboring Genes May Compromise Medical Privacy (Huerta-Sanchez Lab, 2022)
- Bigness, Integrating Long-Range Regulatory Interactions to Predict Gene Expression Using Graph Convolutional Networks (Singh Lab and Larschan Lab, 2022)
- Bonakdar, Gut Commensals Expand Vitamin A Metabolic Capacity of the Mammalian Host (Vaishnava Lab, 2022)
- Smith, Enrichment Analyses Identify Shared Associations for 25 Quantitative Traits in Over 600,000 Individuals from 7 Diverse Ancestries (Ramachandran Lab and Crawford Lab, 2022)
- Babcock, Adult Hippocampal Neurogenesis in Aging and Alzheimer's Disease (Webb Lab, 2021)
- Banuelos, The History and Evolution of the Denisovian-EPAS1 Haplotype in Tibetans (Huerta-Sanchez Lab, 2021)
- Banuelos, Our Tangled Family Tree: New Genomic Methods Offer Insight into the Legacy of Archaic Admixture (Huerta-Sanchez Lab, 2021)
- Smith, Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics (Ramachandran Lab, 2020)
- Penumutchu, Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome (Belenky Lab, 2019)
- Penumutchu, Microbial Competition between Escherichia coli and Candida albicans Reveals a Soluble Fungicidal Factor (Belenky Lab, 2018)
Pre-Prints
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