{"title":"Predicting protein subcellular locations for Gram-negative bacteria using neural networks ensemble","authors":"Junwei Ma, Wenqi Liu, Hong Gu","doi":"10.1109/CIBCB.2009.4925716","DOIUrl":null,"url":null,"abstract":"Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we design seven different training functions based on Elman networks, and use a genetic algorithm to select the proper networks for an ensemble. Experimental results show that the neural networks ensemble has a dominant advantage in performance.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBCB.2009.4925716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we design seven different training functions based on Elman networks, and use a genetic algorithm to select the proper networks for an ensemble. Experimental results show that the neural networks ensemble has a dominant advantage in performance.