Predicting drug-target interactions by dual-network integrated logistic matrix factorization
نویسندگان
چکیده
منابع مشابه
Predicting drug-target interactions by dual-network integrated logistic matrix factorization
In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matr...
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Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show pheno...
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In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approache...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2017
ISSN: 2045-2322
DOI: 10.1038/srep40376