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Machine Learning approaches for Drug target interaction prediction

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  • Thu, 12/14/2017 - 3:00pm - 4:30pm

Machine Learning approaches for Drug target interaction prediction

PhD. Comprehensive Exam by:

Hetal Rahul Rajpura

Date: Thursday, December 14th, 2017
Time: 3:00 9m
Location: Lambton Tower, 3105      

Abstract: In-silico based approaches to drug-target interaction prediction use computational methods as a cheaper and time-effective solution towards the discovery of new drugs / new targets as well as uncover previously unknown drug target interactions. Availability of heterogeneous databases containing comprehensive bio-informatics, chemo-informatics and chemo-genomic data related to drugs, targets and drug target interactions have further fuelled the research for novel predictions to a larger scale.  Biochemical information from potential drug compounds and genomic, transcriptomic, inter-atomic information from potential protein targets can be combined using machine learning approaches to derive new drug-target interactions. Some of the issues as observed in the literature include lack of negative interactions and small sample of positive drug target interactions thereby causing class imbalance. This presentation will cover the current approaches for predicting novel interactions like, similarity based approach, feature vector based approach and the very popular matrix factorization based approach. Also, we shall introduce a recent PU –Learning based approach, which is positive and unknown labelled learning based method to predict new interactions from positive and unknown datasets.

Thesis Committee:    
Internal Reader: Dr Luis Rueda, Dr Mehdi Kargar  
External Reader: Dr Siyaram Pandey
Advisor: Dr Alioune Ngom

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