文章基本信息

标题：Predicting Transmembrane β-barrel Segments with Chain Learning and Sparse Coding
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作者：Yin Xi ; Shen Hong-Bin
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：28
页码：245-250
DOI：10.1016/j.ifacol.2015.12.133
语种：English
出版社：Elsevier
摘要：AbstractWe developed a novel method named MemBrain-TMB to predict the spanning segments of transmembrane Â-barrel from amino acid sequence. MemBrain-beta is a statistical machine learningbased model, which is constructed using a new chain learning algorithm with the input features are encoded by the image sparse representation approach. To deal with the diverse loop length problem, we applied a dynamic threshold method, which is particularly useful for enhancing the recognition of short loops and tight turns. MemBrain-TMB achieves a Q2 accuracy of 93% and SOV of 97% on the benchmark dataset, which is 5%~10% higher than other existing predictors.
关键词：KeywordsMachine learningPattern recognitionImage processingPrediction methodsEvaluation