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

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9.6 Performance of HybridGO-Loc

9.6.1 Comparing different features

Figure 9.5a shows the performance of individual and hybridized GO features on the virus dataset based on leave-one-out cross validation (LOOCV). In the figure, SS1, SS2, and SS3 represent Lin’s, Jiang’s, and RS similarity measures, respectively. Hybrid1, Hybrid2, and Hybrid3 represent the hybridized features obtained from these measures. As can be seen, in terms of all six performance metrics, the performance of the hybrid features is remarkably better than the performance of individual features. Specifically, the OAAs (the most stringent and objective metric) of all of the three hybrid features are at least 3% (absolute) higher than that of the individual features, which suggests ...

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