Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization.
Drug-target interactions provide useful information for biomedical drug discovery as well as drug development. However, it is costly and time consuming to find drug-target interactions by experimental methods. As a result, developing computational approaches for this task is necessary and has practi...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/28/2021
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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=149509634&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149509634 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/28/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149509634 149509634 149509634 10.1155/2021/5599263 149509634 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization. aug: au: Wang, Aizhen Wang, Minhui affil: Department of Pharmacy, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, Huai'an 223002, China sug: subj: Drug Discovery Methods Bioinformatics Methods Algorithms Human Genomics Drug Design Machine Learning Validity Models, Statistical Computer Simulation Benchmarking Molecular Structure Drug Compounding ab: Drug-target interactions provide useful information for biomedical drug discovery as well as drug development. However, it is costly and time consuming to find drug-target interactions by experimental methods. As a result, developing computational approaches for this task is necessary and has practical significance. In this study, we establish a novel dual Laplacian graph regularized logistic matrix factorization model for drug-target interaction prediction, referred to as DLGrLMF briefly. Specifically, DLGrLMF regards the task of drug-target interaction prediction as a weighted logistic matrix factorization problem, in which the experimentally validated interactions are allocated with larger weights. Meanwhile, by considering that drugs with similar chemical structure should have interactions with similar targets and targets with similar genomic sequence similarity should in turn have interactions with similar drugs, the drug pairwise chemical structure similarities as well as the target pairwise genomic sequence similarities are fully exploited to serve the matrix factorization problem by using a dual Laplacian graph regularization term. In addition, we design a gradient descent algorithm to solve the resultant optimization problem. Finally, the efficacy of DLGrLMF is validated on various benchmark datasets and the experimental results demonstrate that DLGrLMF performs better than other state-of-the-art methods. Case studies are also conducted to validate that DLGrLMF can successfully predict most of the experimental validated drug-target interactions. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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