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• W2895810213 abstract "DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from the protein sequence only to classify the DBPs. Most of the methods work effectively on the train sets but performance of most of them degrades in the independent test set. It shows a room for improving the prediction method by reducing over-fitting. In this paper, we have extracted several features solely using the protein sequence and carried out two different types of feature selection on them. Our results have proven comparable on training set and significantly improved on the independent test set. On the independent test set our accuracy was 82.26% which is 1.62% improved compared to the previous best state-of-the-art methods. Performance in terms of sensitivity and area under receiver operating characteristic curve for the independent test set was also higher and they were 0.95 and 0.823 respectively." @default.
• W2895810213 created "2018-10-26" @default.
• W2895810213 creator A5030913401 @default.
• W2895810213 creator A5033587411 @default.
• W2895810213 creator A5067504579 @default.
• W2895810213 date "2019-01-01" @default.
• W2895810213 modified "2023-09-25" @default.
• W2895810213 title "Effective DNA binding protein prediction by using key features via Chou’s general PseAAC" @default.
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• W2895810213 doi "https://doi.org/10.1016/j.jtbi.2018.10.027" @default.
• W2895810213 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30316822" @default.
• W2895810213 hasPublicationYear "2019" @default.
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