Machine Learning for Protein Subcellular Localization Prediction

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

Shibiao Wan, Man-Wai Mak, Hong Kong Polytechnic University, Hong Kong.