ArticleBioinformatics and biology insights2009
Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines.
Article in Bioinformatics and biology insights, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.BMC bioinformatics · 2018Article
- Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.Chemical Society reviews · 2015Review
- Understanding the undelaying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein.PloS one · 2014Article
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Authors and funding
6 authors.
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Abstract
Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.
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