Background: DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering\nstudies, the identification of proteins with certain functions has become increasingly important and needs to be\nperformed rapidly and efficiently. In previous years, several approaches have been developed to improve the\nidentification of DNA-binding proteins. However, the currently available resources are insufficient to accurately\nidentify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy\nrate and the low identification success of the current methods.\nResults: In this paper, we explored the practicality of modelling DNA binding identification and simultaneously\nemployed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is\ncomprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble\nclassifier designated as imDC. Experiments using different datasets showed that our method is more successful than\nthe traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature\nthat selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an\nArea Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test data set was tested in our\nmethod and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.\nConclusions: Our method can help to accurately identify DNA-binding proteins, and the web server is accessible at\nhttp://data mining. xmu.edu.cn/~songli/nDNA. In addition, we also predicted possible DNA-binding protein sequences\nin all of the sequences from the UniProtKB/Swiss-Prot database.
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