AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment
Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.
Overview
Abstract
Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations. We show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single-molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome-wide scale. We also find that the same linear relationship holds for transcription at enhancer elements.
Brief Biography
Trisevgeni Rapakoulia is a Ph.D. candidate at King Abdullah University of Science and Technology supervised by Prof. Xin Gao. Broad areas of interest include bioinformatics and machine learning. Research field: study the effects of drugs and drug combinations on the transcriptome level. For more info contact: Robert Hoehndorf: email: robert.hoehndorf@kaust.edu.sa
More Information:
Light lunch will be provided.
