Machine Learning Kernel Methods for Protein Function Prediction

Biological information in protein is still remaining as a big challenge today. Many protein surfaces, bound only with protein due to its broad range function properties. Protein function prediction is critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying protein function to better understand the disease, drug designs, genome sequencing and for other research. Conventional methods are tedious and costly. This reasons still need a computational tool to predict and identify various protein function. Recent research practice used to developed machine learning methods to classifying model to correctly identifications from huge protein data source due to these reasons protein datasets and their vector space is in large sizes. The SVM classifier is hard to train a broad range sequence of protein datasets. Therefore, classifier SVM algorithms cannot handle to train large support vectors. In this study is using the kernel, optimize the classifier support vector and enhance the classification accuracy. Classification results of functional protein classes are found to be in RBF kernel is 97.27%.