| Summary: | 碩士 === 國立中興大學 === 基因體暨生物資訊學研究所 === 101 === Protein subcellular localization is an important part of biological research; which could support drug development and explore the function of proteins. Many subcellular localization prediction tools has developed, most of them used the data of eukaryotes or prokaryotes for model training, however, the related predictors for human proteins are rare.
We established a system to predict subcellular localization of human proteins with Singleplex and Multiplex, called REALoc. It based on two layers architecture integrated with two different machine learning methods, one-to-one and many-to may. Besides, system included many sequence based features and function based features, such as amino acid composition, surface accessibility. In addition, we developed a series of computing features like weighted sign AAindex, sequence similarity profile and regular-mRMR feature selection for Gene Ontology. 5 folds Cross-validation was performed with iLoc-Hum on training dataset covers 6 location sites (Cell membrane, Cytoplasm, Endoplasmic reticulum/Golgi apparatus, Mitochondrion, Nucleus, secreted), overall absolute true success rate of REALoc is 75.34%, and on testing dataset is 57.14% which performances are about 10% higher than other four prediction systems. Finally, this study discussed the performance of the two decision mechanism of vote and GANN for predicting single location and multiple locations. Furthermore, the relationship between the protein-protein interaction and subcellular localization by using motifs was investigated.
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