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

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9 Results and analysis

This chapter will elaborate the experimental results and related analysis for all the predictors introduced in previous chapters, including GOASVM and FusionSVM for single-location protein subcellular localization, and mGOASVM, AD-SVM, mPLR-Loc, SS-Loc, HybridGO-Loc, RP-SVM, and R3P-Loc for multi-location protein subcellular localization.

9.1 Performance of GOASVM

9.1.1 Comparing GO vector construction methods

Table 9.1 shows the performance of different GO-vector construction methods on the EU16, HUM12, and the novel eukaryote (NE16) datasets, which were detailed in Tables 8.1, 8.2, and 8.3, respectively. Linear SVMs were used for all cases, and the penalty factor was set to 0.1. For the EU16 and HUM12 datasets, leave-one-out ...

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