CWLy-pred: A novel cell wall lytic enzyme identifier based on an improved MRMD feature selection method.

Abstract Cell wall lytic enzymes play key roles in biochemical, morphological, genetic research and industry fields. To save time and labor costs， bioinformatic methods are usually adopted to narrow the scope of in vitro experimentation. In this paper, we established a novel machine learning (support vector machine) based identifier called CWLy-pred to identify cell wall lytic enzymes. An improved MRMD feature selection method is also proposed to select the optimal training set to avoid data redundancy. CWLy-pred obtains an accuracy of 93.067%, a sensitivity of 85.3%, a specificity of 94.8%, an MCC of 0.775 and an AUC of 0.900. It outperforms the state-of-the-art identifier in terms of accuracy, sensitivity, specificity and MCC. Our proposed model is based on a feature set of only 6 dimensions; therefore, it not only can overcome overfitting problems but can also supervise biological experiments effectively. CWLy-pred is embedded in a web application at http://server.malab.cn/CWLy-pred/index.jsp , which is accessible for free.