Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions.
A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industr...
| Publicado en: | BioMed Research International Vol. 2013; pp. 485034 - 485035 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| 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=104101201&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104101201 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104101201 104101201 2012330608 NLM24078917 PMC3776367 104101201 ppf: 485034 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions. aug: au: Chen, Lei Huang, Tao Zhang, Jian Zheng, Ming-Yue Feng, Kai-Yan Cai, Yu-Dong Chou, Kuo-Chen affil: College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. sug: subj: Drug Interactions Adverse Drug Event Drugs Metabolism Proteins Metabolism Databases Human ab: A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By ajackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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