pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC

Motivation: A cell contains numerous protein molecules. One of the fundamental goals in cell biology is to determine their subcellular locations, which can provide useful clues about their functions. Knowledge of protein subcellular localization is also indispensable for prioritizing and selecting the right targets for drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called 'pLoc-mAnimal' was developed for identifying the subcellular localization of animal proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with the 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-mAnimal was trained by an extremely skewed dataset in which some subset (subcellular location) was about 128 times the size of the other subsets. Accordingly, such an uneven training dataset will inevitably cause a biased consequence. Results: To alleviate such biased consequence, we have developed a new and bias-reducing predictor called pLoc_bal-mAnimal 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-mAnimal, the existing state-of-the-art predictor, in identifying the subcellular localization of animal proteins.