Faisal, Muhammad Reza and Abipihi, Bahriddin and Nguyen, Ngoc G and Purnama, Bedy and Delimayanti, Mera K and Phan, Dau and Lumbanraja, Favorisen R and Kubo, Mamoru and Satou, Kenji (2018) Improving Protein Sequence Classification Performance Using Adjacent and Overlapped Segments on Existing Protein Descriptors. Journal of Biomedical Science and Engineering, 11 (6). pp. 126-143. ISSN 1937-6871
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Abstract
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.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer |
| Depositing User: | Favorisen R Lumbanraja |
| Date Deposited: | 16 Nov 2018 17:22 |
| Last Modified: | 16 Nov 2018 17:22 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/9594 |
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