Integrative analysis of genomic data in complex disease

As a postdoctoral fellow, I lead and/or participate in several projects aimed at understanding the mechanisms behind broad molecular changes (e.g., thousands of differentially expressed genes between normal and disease tissues). For example, my colleagues and I identified transcription factor binding sites with altered DNAm in prostate cancer and genomic regulation of triple negative breast cancer invasion. Likewise, by measuring relative cell proliferation from gene expression data across 19 cancers, my colleagues and I demonstrated cell proliferation is a major driver of gene expression variation, is strongly associated with clinical characteristics, and by stratifying on cell proliferation, improved predictive models of patient survival. Additionally, we analyzed gene expression and metabolite changes in psychiatric disease and found that while disease differences were heterogeneous across disease samples, they were highly correlated with transcripts previously identified as specific to brain cell types. Many of these analyses utilize machine learning techniques.


. Post-mortem molecular profiling of three psychiatric disorders. Genome Medicine, 2017.

Preprint PDF Dataset Project Poster

. Genome-wide DNA methylation measurements in prostate tissues uncovers novel prostate cancer diagnostic biomarkers and transcription factor binding patterns. BMC Cancer, 2017.

PDF Dataset Project

. Genomic regulation of invasion by STAT3 in triple negative breast cancer. Oncotarget, 2017.

PDF Dataset Project