Sequence Similarity and Support Vector Machine for Protein Secondary Structure Prediction

I The majority of human coding regions have been sequenced and several genome sequencing projects have been completed. With the growth of large-scale sequencing data, an efficient approach to analyze protein is more important. Protein function and structure are foundations for drug design and protein-based product. However, it’s difficult to predict protein function and structure (three-dimension) directly from protein sequence. Therefore, analyzing protein secondary structure is indispensable. In the previous work, researchers always focused on classifying three states of protein secondary structure: helix, strand and coil classes. It’s a common classification problem for the prediction of protein secondary structure. Comparing with other machine learning methods for this problem, many studies usually ignore the protein local sequence/structure properties. It concerns the accuracy of prediction because there exists a large number of proteins that are homologous but whose sequences are only remotely related. In this thesis, we propose to use sequence similarity an