Drugrepositioning is the process of

Topic: EnvironmentWater Pollution
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Last updated: December 31, 2019

Drugrepositioning is the process of recycling ex- isting drugs for new indicationsby identifying the potential drug-target interactions (DTIs). However, predicting new associationsbetween drugs and target proteins is a challenging issue because the number ofknown interacting drug-target pairs is much smaller than that ofnon-interacting drug-target pairs which have not been experimentally verifiedto be true negative samples. Moreover, theexplosive growth in genomic sequence and chemical structure data motivate us todevelop an effective computational method to accurately predict a noveldrug-target interaction. In this paper, wepropose an algorithmic framework based on deep semi supervised learning calledDSSL-DTIs to potentially overcome these limitations.

Firstly, we use thepowerful technique of stacked autoencoder to convert high- dimensional featuresto low-dimensional representations. Then, we apply unsupervised stackedautoencoder model for initializingthe weights of a supervised deep neural networks model. The proposed approachhas been comparedwith other state-of-the-art methods applied all onthe same reference datasets of Drug-Bank.Preliminary performance assessment results have shown that our approachoutperforms these techniques. Its overall accuracy performance is more than98%. The DSSL-DTIs can be used     topredict large-scale new drug-target interactions.

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The obtained results haveshown that highly ranked candidate DTIs obtained from DeepSS-DTIs are alsopresent in the DrugBank database and in the literature which demonstrates theeffectiveness of the proposed approach.

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