Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naive Bayes.

ABSTRACT: Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regressio

SUBMITTER: Lou W 

PROVIDER: S-EPMC3901691 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature