Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.
Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.
Shibiao Wan, Man-Wai Mak, Hong Kong Polytechnic University, Hong Kong.
Inhaltsangabe
1 Introduction 1.1 Proteins and Their Subcellular Locations 1.2 Why Computationally Predicting Protein Subcellular Localization? 1.3 Organization of The Thesis
2 Literature Review 2.1 Sequence-Based Methods 2.2 Knowledge-Based Methods 2.3 Limitations of Existing Methods
3 Legitimacy of Using Gene Ontology Information 3.1 Direct Table Lookup? 3.2 Only Using Cellular Component GO Terms? 3.3 Equivalent to Homologous Transfer? 3.4 More Reasons for Using GO Information
4 Single-Location Protein Subcellular Localization 4.1 GOASVM: Extracting GO from Gene Ontology Annotation Database 4.2 FusionSVM: Fusion of Gene Ontology and Homology-Based Features 4.3 Summary
5 From Single-Location to Multi-Location 5.1 Significance of Multi-Location Proteins 5.2 Multi-Label Classification 5.3 mGOASVM: A Predictor for Both Single- and Multi-Location Proteins 5.4 AD-SVM: An Adaptive-decision Multi-Label Predictor 5.5 mPLR-Loc: A Multi-Label Predictor Based on Penalized Logistic- Regression 5.6 Summary
6 Mining Deeper on GO for Protein Subcellular Localization 6.1 Related Work 6.2 SS-Loc: Using Semantic Similarity Over GO 6.3 HybridGO-Loc: Hybridizing GO Frequency and Semantic Similarity Features 6.4 Summary
7 Ensemble Random Projection for Large-Scale Predictions 7.1 Related Work 7.2 RP-SVM: A Multi-Label Classifier with Ensemble Random Projection 7.3 R3P-Loc: A Predictor Based on Ridge Regression and Random Projection 7.4 Summary
8 Experimental Setup 8.1 Prediction of Single-Label Proteins 8.2 Prediction of Multi-Label Proteins 8.3 Statistical Evaluation Methods 8.4 Summary
9 Results and Analysis 9.1 Performance of GOASVM 9.2 Performance of FusionSVM 9.3 Performance of mGOASVM 9.4 Performance of AD-SVM 9.5 Performance of mPLR-Loc 9.6 Performance of SS-Loc 9.7 Performance of HybridGO-Loc 9.8 Performance of Performance of RP-SVM 9.9 Performance of R3P-Loc 9.10 Comprehensive Comparison of Proposed Predictors 9.11 Summary
10 Discussions 10.1 Analysis of Single-label Predictors 10.2 Advantages of mGOASVM 10.3 Analysis for HybridGO-Loc 10.4 Analysis for RP-SVM 10.5 Comparing the Proposed Multi-Label Predictors 10.6 Summary
11 Conclusions A Web-Servers for Protein Subcellular Localization B Proof of No Bias in LOOCV Bibliography
1 Introduction 1.1 Proteins and Their Subcellular Locations 1.2 Why Computationally Predicting Protein Subcellular Localization? 1.3 Organization of The Thesis
2 Literature Review 2.1 Sequence-Based Methods 2.2 Knowledge-Based Methods 2.3 Limitations of Existing Methods
3 Legitimacy of Using Gene Ontology Information 3.1 Direct Table Lookup? 3.2 Only Using Cellular Component GO Terms? 3.3 Equivalent to Homologous Transfer? 3.4 More Reasons for Using GO Information
4 Single-Location Protein Subcellular Localization 4.1 GOASVM: Extracting GO from Gene Ontology Annotation Database 4.2 FusionSVM: Fusion of Gene Ontology and Homology-Based Features 4.3 Summary
5 From Single-Location to Multi-Location 5.1 Significance of Multi-Location Proteins 5.2 Multi-Label Classification 5.3 mGOASVM: A Predictor for Both Single- and Multi-Location Proteins 5.4 AD-SVM: An Adaptive-decision Multi-Label Predictor 5.5 mPLR-Loc: A Multi-Label Predictor Based on Penalized Logistic- Regression 5.6 Summary
6 Mining Deeper on GO for Protein Subcellular Localization 6.1 Related Work 6.2 SS-Loc: Using Semantic Similarity Over GO 6.3 HybridGO-Loc: Hybridizing GO Frequency and Semantic Similarity Features 6.4 Summary
7 Ensemble Random Projection for Large-Scale Predictions 7.1 Related Work 7.2 RP-SVM: A Multi-Label Classifier with Ensemble Random Projection 7.3 R3P-Loc: A Predictor Based on Ridge Regression and Random Projection 7.4 Summary
8 Experimental Setup 8.1 Prediction of Single-Label Proteins 8.2 Prediction of Multi-Label Proteins 8.3 Statistical Evaluation Methods 8.4 Summary
9 Results and Analysis 9.1 Performance of GOASVM 9.2 Performance of FusionSVM 9.3 Performance of mGOASVM 9.4 Performance of AD-SVM 9.5 Performance of mPLR-Loc 9.6 Performance of SS-Loc 9.7 Performance of HybridGO-Loc 9.8 Performance of Performance of RP-SVM 9.9 Performance of R3P-Loc 9.10 Comprehensive Comparison of Proposed Predictors 9.11 Summary
10 Discussions 10.1 Analysis of Single-label Predictors 10.2 Advantages of mGOASVM 10.3 Analysis for HybridGO-Loc 10.4 Analysis for RP-SVM 10.5 Comparing the Proposed Multi-Label Predictors 10.6 Summary
11 Conclusions A Web-Servers for Protein Subcellular Localization B Proof of No Bias in LOOCV Bibliography
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