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Article: Drug Side-effect Profiles Prediction: From Empirical to Structural Risk Minimization

TitleDrug Side-effect Profiles Prediction: From Empirical to Structural Risk Minimization
Authors
KeywordsDrug side-effect
regularized regression model
support vector machine
Generalized T-Student (GTS) kernel
Issue Date2020
PublisherIEEE.
Citation
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, v. 17 n. 2, p. 402-410 How to Cite?
AbstractThe identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy. In order to dig inside how drug structure will associate with side effect, we further propose weighted GTS (Generalized T-Student Kernel: WGTS) SVM model from a structural risk minimization perspective. The SVM model proposed in this paper provides a better understanding of drug side-effects in the process of drug development. The usefulness of the WGTS model lies in the superior performance in a cross validation setting on 888 approved drugs with 1385 side-effects profiling from SIDER database. This work is expected to shed light on intriguing studies that predict potential un-identifying side-effects and suggest how we can avoid drug side-effects by the removal of some distinguished chemical structures.
Persistent Identifierhttp://hdl.handle.net/10722/288093
ISSN
2019 Impact Factor: 3.015
2015 SCImago Journal Rankings: 0.706

 

DC FieldValueLanguage
dc.contributor.authorJiang, H-
dc.contributor.authorQiu, Y-
dc.contributor.authorHOU, W-
dc.contributor.authorCHENG, X-
dc.contributor.authorYim, MY-
dc.contributor.authorChing, WK-
dc.date.accessioned2020-10-05T12:07:46Z-
dc.date.available2020-10-05T12:07:46Z-
dc.date.issued2020-
dc.identifier.citationIEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, v. 17 n. 2, p. 402-410-
dc.identifier.issn1545-5963-
dc.identifier.urihttp://hdl.handle.net/10722/288093-
dc.description.abstractThe identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy. In order to dig inside how drug structure will associate with side effect, we further propose weighted GTS (Generalized T-Student Kernel: WGTS) SVM model from a structural risk minimization perspective. The SVM model proposed in this paper provides a better understanding of drug side-effects in the process of drug development. The usefulness of the WGTS model lies in the superior performance in a cross validation setting on 888 approved drugs with 1385 side-effects profiling from SIDER database. This work is expected to shed light on intriguing studies that predict potential un-identifying side-effects and suggest how we can avoid drug side-effects by the removal of some distinguished chemical structures.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE/ACM Transactions on Computational Biology and Bioinformatics-
dc.rightsIEEE/ACM Transactions on Computational Biology and Bioinformatics. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectDrug side-effect-
dc.subjectregularized regression model-
dc.subjectsupport vector machine-
dc.subjectGeneralized T-Student (GTS) kernel-
dc.titleDrug Side-effect Profiles Prediction: From Empirical to Structural Risk Minimization-
dc.typeArticle-
dc.identifier.emailChing, WK: wching@hku.hk-
dc.identifier.authorityChing, WK=rp00679-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TCBB.2018.2850884-
dc.identifier.pmid29994681-
dc.identifier.hkuros314730-
dc.identifier.volume17-
dc.identifier.issue2-
dc.identifier.spage402-
dc.identifier.epage410-
dc.publisher.placeUnited States-
dc.identifier.issnl1545-5963-

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