Bioinformatics · 2017 · 208 citations · 26 references
Life SciencesBioinformaticsComputational BiologyMachine LearningSequence AnalysisProtein IdentificationProtein Sequence AnalysisAttributesMachine Learning Prediction ModelsMatrix ScoringPosition-specific ScoringPosition-specific Scoring MatricesScoring MatricesSequence Similarity ScoringWeb Server Architecture
SUMMARY: Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to improve the performance of various predictors of protein attributes. Even though a number of algorithms have been proposed in previous studies, there is currently no universal web server or toolkit available for generating this wide variety of descriptors. Here, we present POSSUM ( Po sition- S pecific S coring matrix-based feat u re generator for m achine learning), a versatile toolkit with an online web server that can generate 21 types of PSSM-based feature descriptors, thereby addressing a crucial need for bioinformaticians and computational biologists. We envisage that this comprehensive toolkit will be widely used as a powerful tool to facilitate feature extraction, selection, and benchmarking of machine learning-based models, thereby contributing to a more effective analysis and modeling pipeline for bioinformatics research. AVAILABILITY AND IMPLEMENTATION: http://possum.erc.monash.edu/ . CONTACT: trevor.lithgow@monash.edu or jiangning.song@monash.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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A large-scale evaluation of computational protein function prediction
Predrag Radivojac, Wyatt T. Clark, Tal Oron et al. · Nature Methods · 2013 · 1.1K citations · Full text
propy: a tool to generate various modes of Chou’s PseAAC
Dongsheng Cao, Qing‐Song Xu, Yi‐Zeng Liang · Bioinformatics · 2013 · 481 citations