Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines.
Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug toxicity in the early stage of drug development. H...
| Publicado en: | Journal of Biomedical Informatics Vol. 68; pp. 132 - 150 |
|---|---|
| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Apr2017
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=122480305&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122480305 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Apr2017 vid: 68 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 122480305 122480305 NLM28286029 122480305 10.1016/j.jbi.2017.03.002 NLM28286029 122480305 ppf: 132 ppct: 18 formats: tig: atl: Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines. aug: au: Tharwat, Alaa Moemen, Yasmine S. Hassanien, Aboul Ella affil: Faculty of Engineering, Suez Canal University, Ismailia, Egypt sug: subj: Adverse Drug Event Drug Discovery Forecasting Algorithms Human ab: Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug toxicity in the early stage of drug development. Hence, there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drugs that biotransformed in liver. The toxic effects were calculated for the current data, namely, mutagenic, tumorigenic, irritant and reproductive effect. Each drug is represented by 31 chemical descriptors (features). The proposed model consists of three phases. In the first phase, the most discriminative subset of features is selected using rough set-based methods to reduce the classification time while improving the classification performance. In the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique (SMOTE), BorderLine SMOTE and Safe Level SMOTE are used to solve the problem of imbalanced dataset. In the third phase, the Support Vector Machines (SVM) classifier is used to classify an unknown drug into toxic or non-toxic. SVM parameters such as the penalty parameter and kernel parameter have a great impact on the classification accuracy of the model. In this paper, Whale Optimization Algorithm (WOA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. The experimental results proved that the proposed model achieved high sensitivity to all toxic effects. Overall, the high sensitivity of the WOA+SVM model indicates that it could be used for the prediction of drug toxicity in the early stage of drug development. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|