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:
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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
Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue. Molecular characterization of the interaction between human IgG and the M-related proteins from Streptococcus pyogenes. Cell type-specific interaction analysis using doublets in scRNA-seq. STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring. Characterizing control of memory CD8 T cell differentiation by BTB-ZF transcription factor Zbtb20. SPECK: an unsupervised learning approach for cell surface receptor abundance estimation for single-cell RNA-sequencing data. Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error. Tumor type and cell type-specific gene expression alterations in diverse pediatric central nervous system tumors identified using single nuclei RNA-seq. Cell type-specific Interaction Analysis using Doublets in scRNA-seq (CIcADA). Promiscuous evolution of Group A Streptococcal M and M-like proteins. |
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