{"title":"利用局部结构预测挖掘蛋白质中的残馀接触","authors":"Mohammed J. Zaki, Shanrong Jin, C. Bystroff","doi":"10.1109/BIBE.2000.889604","DOIUrl":null,"url":null,"abstract":"In this paper, we develop data mining techniques to predict 3D contact potentials among protein residues (or amino acids) based on the hierarchical nucleation-propagation model of protein folding. We apply a hybrid approach, using a hidden Markov model (HMM) to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy results better than those reported previously.","PeriodicalId":196846,"journal":{"name":"Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"57","resultStr":"{\"title\":\"Mining residue contacts in proteins using local structure predictions\",\"authors\":\"Mohammed J. Zaki, Shanrong Jin, C. Bystroff\",\"doi\":\"10.1109/BIBE.2000.889604\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we develop data mining techniques to predict 3D contact potentials among protein residues (or amino acids) based on the hierarchical nucleation-propagation model of protein folding. We apply a hybrid approach, using a hidden Markov model (HMM) to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy results better than those reported previously.\",\"PeriodicalId\":196846,\"journal\":{\"name\":\"Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"57\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2000.889604\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2000.889604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mining residue contacts in proteins using local structure predictions
In this paper, we develop data mining techniques to predict 3D contact potentials among protein residues (or amino acids) based on the hierarchical nucleation-propagation model of protein folding. We apply a hybrid approach, using a hidden Markov model (HMM) to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy results better than those reported previously.