Experienced Computational Biologist with over a decade of experience in Cancer Genomics, including working within the TCGA consortium as part of the integrative genomics team while at the Sander lab in MSKCC. Particularly adept at interdisciplinary collaborations, with extensive experience of collaborating with Wet-Lab Biologists, Physician Scientists, and Computational Biologists.
Extensive research on evaluating cell lines as tumor models using genomic profiles, including two first-author papers published in Nature Communications (PMID: 23839242, PMID: 28489074), and a third first-author paper published in Cell Reports Methods (https://www.sciencedirect.com/science/article/pii/S2667237521000849).
Previous work includes –
Development of a method to compare tumor samples using weighted similarity, with weights chosen to emphasize biological properties of interest in a given study.
Evaluation of Cell Lines (based on genomic similarity to tumors)
Integrative analysis of genomics data (including mutations, CNAs, mRNA expression data etc.)
Multiple projects as part of the TCGA consortium
Analyzing targeted genomic sequencing data for personalized therapy
Nominating novel candidates for targeted therapy in multiple cancers
Analysis of proteomics data as part of the CPTAC consortium
Predicting drug sensitivity in cell lines
Copy Number Aberration (CNA) analysis
Comparison of metastatic and primary tumors
Cancer driver gene discovery and evaluation
Prediction of alternative splicing using machine learning
Analysis of conservation of tandem alternative splice sites
Creation of database and tools of tandem alternative splicing (www.tassdb.info)