Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method
DC Field | Value | Language |
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dc.contributor.author | Sim, J | - |
dc.contributor.author | Kim, SY | - |
dc.contributor.author | Lee, J | - |
dc.date.available | 2018-05-10T17:39:15Z | - |
dc.date.created | 2018-04-17 | - |
dc.date.issued | 2005-06-15 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/19361 | - |
dc.description.abstract | Motivation: The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. Results: We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1 % accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a bench-marking dataset with 229 proteins. | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.relation.isPartOf | BIOINFORMATICS | - |
dc.subject | SECONDARY STRUCTURE PREDICTION | - |
dc.subject | SUPPORT VECTOR MACHINES | - |
dc.subject | PATTERN-RECOGNITION | - |
dc.subject | SEQUENCE ALIGNMENT | - |
dc.subject | FOLD RECOGNITION | - |
dc.subject | DECISION-MAKING | - |
dc.subject | SEGMENTATION | - |
dc.subject | CLASSIFIER | - |
dc.subject | REGRESSION | - |
dc.subject | ALGORITHM | - |
dc.title | Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method | - |
dc.type | Article | - |
dc.identifier.doi | 10.1093/bioinformatics/bti423 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.21, no.12, pp.2844 - 2849 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000229934600009 | - |
dc.identifier.scopusid | 2-s2.0-20844452070 | - |
dc.citation.endPage | 2849 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 2844 | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 21 | - |
dc.contributor.affiliatedAuthor | Lee, J | - |
dc.type.docType | Article | - |
dc.description.oadoiVersion | published | - |
dc.subject.keywordPlus | SECONDARY STRUCTURE PREDICTION | - |
dc.subject.keywordPlus | SUPPORT VECTOR MACHINES | - |
dc.subject.keywordPlus | PATTERN-RECOGNITION | - |
dc.subject.keywordPlus | SEQUENCE ALIGNMENT | - |
dc.subject.keywordPlus | FOLD RECOGNITION | - |
dc.subject.keywordPlus | DECISION-MAKING | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | CLASSIFIER | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.description.journalRegisteredClass | scopus | - |
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