Methods development for "Omics" data

National Institute Of Environmental Health Sciences

Investigators

Linked publications, trials & patents

Abstract

Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy. With my student Jun Ma, we developed a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. The use of dimension reduction dramatically decreases the computation time, without losing accuracy. Ongoing projects building onto methods for detecting gene-environment interactions are currently ongoing, using variance QTLs to prioritize single nucleotide polymorphisms for detecting gene-gene interactions. Additionally, Dr. Ziyue Wang is working on developing new normalization approaches for microbiome data. We have also established collaborations with Dr. Anchang's group on methods for single cell data analysis.