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DPP-PseAAC

swMATH ID: 24764
Software Authors: Rahman, M.Saifur; Shatabda, Swakkhar; Saha, Sanjay; Kaykobad, M.; Rahman, M.Sohel
Description: DPP-PseAAC: a DNA-binding protein prediction model using Chou’s general pseaac. A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as {it DNA-binding Protein Prediction model using Chou’s general PseAAC (DPP-PseAAC)}, demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21
Homepage: https://github.com/srautonu/DNABinding
Source Code:  https://github.com/srautonu/DNABinding
Keywords: DNA binding; classification; prediction; support vector machine; random forest; PseAAC
Related Software: pLoc-mPlant; pLoc-mEuk; pLoc-mVirus; pLoc-mAnimal; iPTM-mLys; iDNA-Prot; Pse-in-One; iEnhancer-2L; pSuc-Lys; 2L-piRNA; iRNA-PseU; iProt-Sub; iPromoter-2L; iRSpot-EL; iRNAm5C-PseDNC; iRNA-PseColl; iRNA-AI; iPhos-PseEn; iPhos-PseEvo; iHyd-PseCp
Cited in: 5 Publications

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