Formulates the prediction of enhancers and their strength as a binary classification problem and solves it using a machine learning algorithm. EnhancerPred extracts features using BPB, NC and PseNc and also takes advantage of efficient feature selection, which was shown here to be robust and high performing using a rigorous jackknife test. In comparison to existing tools, EnhancerPred achieved satisfactory MCC values, especially for the prediction of whether an enhancer has a strong or weak effect on gene expression.
Department of Mathematics, Dalian Maritime University, Dalian, China
EnhancerPred funding source(s)
This work was supported by the Fundamental Research Funds for the Central Universities under grant (number 3132014324, 3132015159) and the Scientific Research Plan of the Department of Education of Liaoning Province under grant (L2014200).