Siming Zhao, PhD
Title(s)
Assistant Professor of Biomedical Data Science
Department(s)
Biomedical Data Science
Education
2010 B.S. Tsinghua University, Beijing
2015 Ph.D. Yale University, New Haven
Programs
Dartmouth Cancer Center
Quantitative Biomedical Sciences
Websites
https:
Contact Information
Email: Siming.Zhao@Dartmouth.edu
Professional Interests
Dr. Zhao's research focuses on studying the genetic etiology of human diseases, in particular, cancer. Her lab develops computational methods and tools to analyze large-scale genomic datasets, aiming to translate data into biological insights. Specific areas of interest include modeling of mutation selection in cancer, genotype-phenotype association analysis, integration of multiple types of genomic datasets for disease gene discovery.
Rotations and Thesis Projects
Methods development for studying mutations in noncoding regions in cancer
Impacts of genetic backgrounds for cancer phenotypes
Causal gene/pathway identification in genome wide association analysis
Biography
Dr. Zhao completed her PhD in Genetics at Yale University and received post-doctoral training at University of Chicago. She has broad training in genetics, cancer biology, bioinformatics and statistical genetics. In the past, she developed computational methods to study the genetics of cancer and other complex diseases. She also also led the analysis of several cancer whole-exome sequencing projects. She is interested in the roles of genetic variations in cancer and computational methods to translate large-scale genomic data into disease mechanisms.
Recent Advances in Radiative Cooling: From Fundamentals to Commercial Applications. Integrating multi-omics and multi-context QTL data with GWAS reveals the genetic architecture of complex traits and improves the discovery of risk genes. MIRAGE: A Bayesian statistical method for gene-level rare-variant analysis incorporating functional annotations. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Integrated mutational landscape analysis of uterine leiomyosarcomas. Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Whole-exome sequencing of cervical carcinomas identifies activating ERBB2 and PIK3CA mutations as targets for combination therapy. Detailed modeling of positive selection improves detection of cancer driver genes. A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies. Silencing of transposable elements may not be a major driver of regulatory evolution in primate iPSCs. |
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