Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-8-300
Title: Efficacy of different protein descriptors in predicting protein functional families
Authors: Ong, S.A.K.
Lin, H.H.
Chen, Y.Z. 
Li, Z.R.
Cao, Z.
Issue Date: 17-Aug-2007
Source: Ong, S.A.K., Lin, H.H., Chen, Y.Z., Li, Z.R., Cao, Z. (2007-08-17). Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinformatics 8 : -. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-8-300
Abstract: Background: Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families. Results: The performance of these descriptor-sets were ranked by Matthews correlation coefficient (MCC), and categorized into two groups based on their performance. While there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. The combination-sets tend to give slightly but consistently higher MCC values and thus overall best performance such that three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets. Conclusion: Our study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors. © 2007 Ong et al; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/105910
ISSN: 14712105
DOI: 10.1186/1471-2105-8-300
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