Predicting Transmembrane β-barrel Segments with Chain Learning and Sparse Coding. Issue 28 (2015)

• Record Type: Journal Article
• Authors: Xi, Yin; Hong-Bin, Shen
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 28(2015)
• Page Start: 245
• Page End: 250
• Publication Date: 2015
• Subjects: Machine learning -- Pattern recognition -- Image processing -- Prediction methods -- Evaluation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.12.133
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: We 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.