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- W2891918406 abstract "One of the hottest topics in molecular cell biology is to determine the subcellular localization of proteins from various different organisms. This is because it is crucially important for both basic research and drug development. Recently, a predictor called “pLoc-mGneg” was developed for identifying the subcellular localization of Gram-negative bacterial proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mGneg was trained by an extremely skewed dataset in which some subset (subcellular location) was about 5 to 70 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To alleviate such a consequence, we have developed a new and bias-reducing predictor called pLoc_bal-mGneg by quasi-balancing the training dataset. Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mGneg, the existing state-of-the-art predictor in identifying the subcellular localization of Gram-negative bacterial proteins. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mGneg/, by which users can easily get their desired results without the need to go through the detailed mathematics." @default.
- W2891918406 created "2018-09-27" @default.
- W2891918406 creator A5011261818 @default.
- W2891918406 creator A5033602780 @default.
- W2891918406 creator A5083281814 @default.
- W2891918406 date "2018-12-01" @default.
- W2891918406 modified "2023-09-25" @default.
- W2891918406 title "pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC" @default.
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