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Drug-target Interaction Network Prediction using Short Linear Motifs

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  • Fri, 02/10/2017 - 1:00pm - 3:00pm

Drug-target Interaction Network Prediction using Short Linear Motifs

MSc Thesis Proposal by:

Wenxiao Xu

Date:  Friday, February 10th, 2017
Time:  1: 00 pm – 3:00 pm
Location: 110, Odette Building

Abstract:  In genomic drug field, drug-target interaction prediction is necessary and valuable research, which can result in the identification of new drugs or novel targets for current drugs (Masoudi-Nejad, Mousavian, Bozorgmehr, 2013). There are several learning-based methods which can be divided into two main categories: similarity-based and feature-based. In this paper, we first use short linear motifs generated from protein sequences in the area of predicting drug-target interactions. The main idea of this thesis is to extract features from protein sequences and drug compounds separately, then combine the features together to do the classification. This method will be tested on four datasets which are Enzyme, GPCR, lon channel and Nuclear receptor.

  Regarding protein features collection, after motifs are obtained using Multiple EM for Motif Elucidation (MEME), we score them in two approaches: I-score which is counting motifs according to the position-specific scoring matrix (PSSM), and Sliding windows. In terms of drug representation, molecular substructure fingerprint defined in PubChem database is an effective descriptor which we use in this paper. Next, apply different classifications on them, and compare the performances.

Thesis Committee:
Internal Reader: Dr. Mehdi Kargar
External Reader: Dr. Dilian Yang
Advisor: Dr. Alioune Ngom & Dr. Luis Rueda

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