| dc.description.abstract | DNA-binding proteins play important role in various processes within the cell.Various machine learning classification algorithms and feature extraction techniques have been used to solve this computational problem of identification of DNA-binding proteins in the last decade. It has many important uses in transcription, DNA replication, compacting the chromosomal DNA, study of antibiotics, drugs, steroids. In this paper, we propose a novel DNA-binding protein prediction method named HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. We have proposed an approach to predict whether a given protein sequence is DNA Binding or not. To the best of our knowledge, this is the first application of HMM profile based features for DNA-binding protein prediction problem. Support Vector Machines(SVM) is used as a classification algorithm. Our method was tested on standard benchmark dataset and was able to improve significantly over the performance of the state-of-the-art methods. We get an accuracy of 86%. | en_US |
