H Robert Frost, PhD
Title(s)
Assistant Professor of Biomedical Data Science
Additional Titles/Positions/Affiliations
Associate Director of Quantitative Biomedical Sciences Graduate Program
Department(s)
Biomedical Data Science
Education
Dartmouth College, Ph.D., 2014
Stanford University, MS, 1995
Stanford University, BS, 1993
Programs
Norris Cotton Cancer Center
Quantitative Biomedical Sciences
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
Selected Publications |
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Intraoperative plasma proteomic changes in cardiac surgery: In search of biomarkers of post-operative delirium. CAMML with the Integration of Marker Proteins (ChIMP). Superkine IL-2 and IL-33 Armored CAR T Cells Reshape the Tumor Microenvironment and Reduce Growth of Multiple Solid Tumors. Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA). Internal oligo(dT) priming introduces systematic bias in bulk and single-cell RNA sequencing count data. CBEA: Competitive balances for taxonomic enrichment analysis. A controlled human infection model of Streptococcus pyogenes pharyngitis (CHIVAS-M75): an observational, dose-finding study. Immune signature of acute pharyngitis in a Streptococcus pyogenes human challenge trial. CAMML: Multi-Label Immune Cell-Typing and Stemness Analysis for Single-Cell RNA-sequencing. Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction. |
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