KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest
Yuran Jia, Shan Huang, Tianjiao Zhang
Abstract
DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.
Topics & Concepts
Random forestIdentification (biology)Computer scienceFeature extractionPattern recognition (psychology)Domain (mathematical analysis)Artificial intelligenceComputational biologyFeature (linguistics)DNAFusionData miningMachine learningBiologyMathematicsGeneticsBotanyMathematical analysisPhilosophyLinguisticsMachine Learning in BioinformaticsRNA and protein synthesis mechanismsProtein Structure and Dynamics