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Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy

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  • Fri, 12/01/2017 - 11:00am - 12:00pm




Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy

Computer Science Colloquium Series
Dr. Laleh Soltan Ghoraie

Date:  Friday, December 1, 2017
Time: 11:00 am
Location: Chrysler Hall North, G100

Abstract: Identification of drug targets and predicting mechanism of action (MoA) for new and uncharacterized anticancer drugs has a crucial role in optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not necessarily available for newly identified or previously uncharacterized small molecules. Therefore, new MoA prediction approaches must be developed to elucidate drug relationships and efficiently classify new compounds with basic input data. I will present Drug Network Fusion (DNF) - a new integrative and scalable computational pharmacogenomic approach. DNF infers drug taxonomies that rely only on minimal prior information, i.e., basic drug characteristics, such as, drug structural information, high-throughput drug perturbation, and drug sensitivity profiles. The resulting integrative drug taxonomies improve characterization of drug target and anatomic classifications compared with taxonomies based on single data types. DNF identifies key drug relationships across different drug categories, providing a flexible and valuable tool for potential clinical applications in precision medicine and drug repurposing in cancer research.

Bio: Laleh received her PhD and MSc degrees in Computer Science from Universities of Waterloo and Windsor, respectively. She is interested in applications of machine learning in healthcare and bioinformatics. During her PhD, she proposed a novel unsupervised method named, Kernelized Partial Canonical Correlation Analysis (KPCCA) for modelling relationships or interactions between multi-dimensional variables, used to model conformation of 3D proteins structure.  She joined Haibe-Kains’ lab at Princess Margaret Cancer Centre (Toronto) in 2015 as a postdoctoral research fellow. She has worked on pharmacogenomics applications in precision medicine such as computational drug repurposing and prediction of drug MoA. She is currently a scientific associate at Haibe-Kains’ lab, and her main research interest is in radiomics, i.e., predicting cancer patients’ survival from radiological imaging data.

 



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