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Machine Learning for Protein Subcellular Localization Prediction by Man-Wai Mak, Shibiao Wan

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11 Conclusions and future directions

11.1 Conclusions

This book presents and proposes a number of GO-based predictors for subcellular localization of both single- and multi-location proteins.

For predicting single-location proteins, the two predictors GOASVM and Fusion-SVM are presented, which differ mainly in the way they retrieve GO information. GOASVM and FusionSVM extract the GO information from the GOA database and the InterProScan, respectively. To enhance the prediction performance, FusionSVM fuses the GO-based predictor – InterProGOSVM – with the profile alignment-based method PairProSVM. Nevertheless, GOASVM still remarkably outperforms FusionSVM. Experimental results also show the superiority of GOASVM over other state-of-the-art single-label ...

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