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• W2609893130 endingPage "e0176486" @default.
• W2609893130 startingPage "e0176486" @default.
• W2609893130 abstract "The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of this study was to retrieve potential drug target proteins (DTPs) from a collected protein dataset, which represents an overwhelming task of great significance. Previously reported methodologies for this task generally employ protein-protein interactive networks but neglect informative biochemical attributes. We formulated a novel framework utilizing biochemical attributes to address this problem. In the framework, a biased support vector machine (BSVM) was combined with the deep embedded representation extracted using a deep learning model, stacked auto-encoders (SAEs). In cases of non-drug target proteins (NDTPs) contaminated by DTPs, the framework is beneficial due to the efficient representation of the SAE and relief of the imbalance effect by the BSVM. The experimental results demonstrated the effectiveness of our framework, and the generalization capability was confirmed via comparisons to other models. This study is the first to exploit a deep learning model for IDTP. In summary, nearly 23% of the NDTPs were predicted as likely DTPs, which are awaiting further verification based on biomedical experiments." @default.
• W2609893130 created "2017-05-05" @default.
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• W2609893130 date "2017-04-28" @default.
• W2609893130 modified "2023-10-17" @default.
• W2609893130 title "A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine" @default.
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• W2609893130 doi "https://doi.org/10.1371/journal.pone.0176486" @default.
• W2609893130 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5409512" @default.
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• W2609893130 hasPublicationYear "2017" @default.
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