H Robert Frost, PhD
Title(s)
Associate Professor of Biomedical Data Science
Associate Professor of Molecular and Systems Biology
Additional Titles/Positions/Affiliations
Associate Director of Quantitative Biomedical Sciences Graduate Program
Department(s)
Biomedical Data Science
Molecular and Systems Biology
Education
Dartmouth College, Ph.D., 2014
Stanford University, MS, 1995
Stanford University, BS, 1993
Programs
Molecular and Cellular Biology Graduate Programs
Dartmouth Cancer Center
Quantitative Biomedical Sciences
Websites
https:
Contact Information
HB 7936
Hanover NH 03755
Office: Rubin 704
Email: Hildreth.R.Frost@Dartmouth.edu
Professional Interests
My research focuses on the development of bioinformatics and biostatistics methods for analyzing high-dimensional genomic data. Areas of statistical interest include dimensionality reduction (e.g., PCA), hypothesis aggregation (e.g., gene set testing), and penalized estimation (e.g., LASSO penalized regression). Areas of biological interest include cell signaling, tissue-specific gene activity, tumor immunology, and cancer prognosis prediction.
Grant Information
NIH grants R35GM146586 and R21CA253408
Courses Taught
QBS 120: Foundations of Biostatistics I, Statistical Theory for the Quantitative Biomedical Sciences
Biological foundation models illuminate annotation blind spots in evolutionarily divergent genomes. Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation. Benchmarking sketching methods on spatial transcriptomics data. Analysis of biological networks using Krylov subspace trajectories. A Composite interaction Score: Prioritizing cell-cell interactions from single-cell RNA-seq with application to pre-menopausal epithelial barriers. Antibody responses against bacterial glycans affinity mature and diversify in germinal centers. Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data. Revealing tissue architecture through the hypercomplex Fourier analysis of spatial transcriptomics data. Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE). Benchmarking sketching methods on spatial transcriptomics data. |
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