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Drug Target Interaction Prediction using PU - Learning

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  • Fri, 03/16/2018 - 11:00am - 12:00pm

Drug Target Interaction Prediction using PU - Learning

Computer Science Colloquium Series

Hetal Rajpura

Date:  Friday, March 16th, 2018
Time: 11:00 am
Location: Odette, 110

Abstract: Predicting new drug target interactions experimentally through wet lab experiments, is time as well as resource intensive. In general, drug-target interaction prediction problem leads to drug discovery, drug repositioning and uncovers exciting patterns in chemogenomics research. Drug and target represent different nodes within a network of interactions. Presence of an edge between the nodes indicates a positive interaction whereas an absence suggests an unknown interaction. Classification based machine learning algorithms are heavily applied in this area of research. Classification algorithms need positive as well as negative data to yield optimised results. The major problem in this field is lack of negative data because the data that are found in the public databases are positive interaction samples. Considering unknown drug target pairs as negative data may cause severe consequences for the prediction performance. Thereby, we propose a positive un-labelled (PU) learning-based approach that uses one class support vector machine as the learning algorithm. The algorithm learns the favourable distribution from the unified feature vector space of drugs and targets and regards unknown pairs as unlabeled instead of labelling them as negative pairs. Additionally, we use 4860 Klekota Roth fingerprint + 881 PubChem fingerprint as a high dimensional and highly discriminative feature vector representation for drugs. To represent protein features, we create a protein-motif matrix based on the sliding window score that records the probability of a motif pattern occurring within a given protein sequence. Also, we separately evaluate the prediction performance using 5-fold nested cross-validation under different experimental setting for each of the four formulations: 1) Known drug-target pair,2) Drug prediction, 3) Target prediction and 4) Unknown drug target pair. We show that our approach yields the best AUC score and outperforms most of the recent works based on one class classifiers and PU-based learning.

Bio:  Hetal Rajpura has B.Tech. and M.Tech. degrees in Computer Science and Engineering from India. She has industrial experience working in Tata Consultancy Services in India. Besides, she was an Assistant Professor in the College of Engineering. She worked as a graduate assistant for different subjects in Computer Science including Computer Architecture, Data structures, and World Wide Web. She is a PhD candidate working in the field of Bioinformatics in the School of Computer Science at the University of Windsor. She is a council member of the Graduate Student Society representing the School of Computer Science. Her research interests focus on drug target interaction prediction using machine learning approaches.

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