{"title":"利用随机森林算法识别蛋白质中的β - α - β基序","authors":"Lixia Sun, Xiuzhen Hu","doi":"10.1109/BMEI.2013.6747001","DOIUrl":null,"url":null,"abstract":"A beta-alpha-beta motif was dataset constructed by using the Definition of Secondary Structure of Proteins (DSSP) and PROMOTIF software, that analyzes a protein coordinate file and provides details about the structural motifs in the protein. We performed a statistical analysis on beta-alpha-beta motifs and non-beta-alpha-beta motifs, and the study objects that loop-helix-loop length was from 10 to 26 amino acids were selected. Hydropathy component of position and amino acid composition were combined as characteristic parameter for expressing the sequence characteristics. A Random Forest algorithm for predicting beta-alpha-beta motifs was developed. The overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieved 88.9% and 0.78.","PeriodicalId":163211,"journal":{"name":"2013 6th International Conference on Biomedical Engineering and Informatics","volume":"217 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Recognition of beta-alpha-beta motifs in proteins by using Random Forest algorithm\",\"authors\":\"Lixia Sun, Xiuzhen Hu\",\"doi\":\"10.1109/BMEI.2013.6747001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A beta-alpha-beta motif was dataset constructed by using the Definition of Secondary Structure of Proteins (DSSP) and PROMOTIF software, that analyzes a protein coordinate file and provides details about the structural motifs in the protein. We performed a statistical analysis on beta-alpha-beta motifs and non-beta-alpha-beta motifs, and the study objects that loop-helix-loop length was from 10 to 26 amino acids were selected. Hydropathy component of position and amino acid composition were combined as characteristic parameter for expressing the sequence characteristics. A Random Forest algorithm for predicting beta-alpha-beta motifs was developed. The overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieved 88.9% and 0.78.\",\"PeriodicalId\":163211,\"journal\":{\"name\":\"2013 6th International Conference on Biomedical Engineering and Informatics\",\"volume\":\"217 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 6th International Conference on Biomedical Engineering and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BMEI.2013.6747001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 6th International Conference on Biomedical Engineering and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEI.2013.6747001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognition of beta-alpha-beta motifs in proteins by using Random Forest algorithm
A beta-alpha-beta motif was dataset constructed by using the Definition of Secondary Structure of Proteins (DSSP) and PROMOTIF software, that analyzes a protein coordinate file and provides details about the structural motifs in the protein. We performed a statistical analysis on beta-alpha-beta motifs and non-beta-alpha-beta motifs, and the study objects that loop-helix-loop length was from 10 to 26 amino acids were selected. Hydropathy component of position and amino acid composition were combined as characteristic parameter for expressing the sequence characteristics. A Random Forest algorithm for predicting beta-alpha-beta motifs was developed. The overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieved 88.9% and 0.78.
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