Improving Protein Sequence Classification Performance Using Adjacent and Overlapped Segments on Existing Protein Descriptors  

作　　者：Mohammad Reza Faisal Bahriddin Abapihi Ngoc Giang Nguyen Bedy Purnama Mera Kartika Delimayanti Dau Phan Favorisen Rosyking Lumbanraja Mamoru Kubo Kenji Satou 

机构地区：[1]Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan [2]Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan

出　　处：《Journal of Biomedical Science and Engineering》2018年第6期126-143,共18页生物医学工程（英文）

摘　　要：In protein sequence classification research, it is popular to convert a variable length sequence of protein into a fixed length numerical vector by using various descriptors, for instance, composition of k-mer composition. Such position-independent descriptors are useful since they are applicable to any length of sequence;however, positional information of subsequence is discarded even though it might have high contribution to classification performance. To solve this problem, we divided the original sequence into some segments, and then calculated the numerical features for them. It enables us to partially introduce positional information (for instance, compositions of serine in anterior and posterior segments of a sequence). Through comprehensive experiments on the number of segments and length of overlapping region, we found our classification approach with sequence segmentation and feature selection is effective to improve the performance. We evaluated our approach on three protein classification problems and achieved significant improvement in all cases which have a dataset with sufficient amino acid in each sequence. This result has shown the great potential of using additional segments in protein sequence classification to solve other sequence problems in bioinformatics.In protein sequence classification research, it is popular to convert a variable length sequence of protein into a fixed length numerical vector by using various descriptors, for instance, composition of k-mer composition. Such position-independent descriptors are useful since they are applicable to any length of sequence;however, positional information of subsequence is discarded even though it might have high contribution to classification performance. To solve this problem, we divided the original sequence into some segments, and then calculated the numerical features for them. It enables us to partially introduce positional information (for instance, compositions of serine in anterior and posterior segments of a sequence). Through comprehensive experiments on the number of segments and length of overlapping region, we found our classification approach with sequence segmentation and feature selection is effective to improve the performance. We evaluated our approach on three protein classification problems and achieved significant improvement in all cases which have a dataset with sufficient amino acid in each sequence. This result has shown the great potential of using additional segments in protein sequence classification to solve other sequence problems in bioinformatics.

关 键 词：PROTEIN SEQUENCE Classification PROTEIN DESCRIPTOR SEQUENCE Segmentation Feature Selection 

分 类 号：R73[医药卫生—肿瘤]