Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble

BMC Bioinformatics
Xiao WangGuo-Zheng Li

Abstract

It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins. In this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels...Continue Reading

References

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Citations

Mar 2, 2017·Molecular BioSystems·Md Al Mehedi HasanMd Khademul Islam Molla
Mar 10, 2017·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Wei ShaoDaoqiang Zhang
Sep 23, 2021·Applied and Environmental Microbiology·Mylène BoulayVincent Juillard

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Software Mentioned

SignalP
Gneg
PSORT
mPLoc
Gpos
iLoc
Proteome Analyst
MultiLoc2
BLAST
ECC

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