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A Comprehensive View on Techniques for Detecting Drug Target Interactions

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  • Fri, 03/24/2017 - 11:00am - 12:00pm

Computer Science Colloquium Series
Hetal Rajpura

Date:  Friday, March 24th, 2017
Time: 11:00 am
Location: Chrysler Hall North, G100

Abstract: Cancer research has drawn different science fields to be studied together in order find effective drug treatments, and hence, to improve cancer care subsequently decrease cancer mortality. The first step in finding new drugs or new targets involve determining new interactions between drug compounds and protein targets. In-silico based approaches to drug-target interaction prediction use computational methods as a cheaper and time-effective solution for the discovery of new cancer drugs or new cancer targets. Biochemical information from potential drug compounds and genomic, transcriptomic, interatomic information from potential protein targets can be combined using machine learning approaches to derive new drug-target interactions. An existing drug compound that is effective for a given disease can also be repurposed (i.e., repositioned) to be used as new drugs for another disease not initially targeted by the drug. Likewise, an existing protein target for a given disease can also be repurposed to been effective target in another disease in which it was not originally a target. My current research involves the study and design machine learning methods to predict new drug-target interaction. My approaches will be based on (1) integrating the knowledge available in existing drug-target interaction databases, disease databases, drug compound databases, and protein target databases, and (2) devise ML algorithms which take the combined knowledge as input and predict whether there is an interaction between a given but arbitrary pair of drug and target. In this talk, I will discuss the existing machine learning approaches for predicting new drug-target interactions. I will also walk through the E-Utility ENTREZ which helps in mining useful biological information from the NCBI database.

Bio: Mrs Hetal Rajpura is currently a PhD student in the UWindsor’s School of Computer Science, under the supervision of Dr. Alioune Ngom since September 2016. She received her MSc degree in 2013 from Lalbhai Dalpatbhai College of Engineering where she also worked as a Lecturer. Prior to completing her MSc degree, she had also worked as an Associate IT Engineer at TATA Consultancy Services in the period 2009-2011. Her research interests include data mining, bioinformatics, R programming language, machine learning, and natural language processing.

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