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Machine Learning for Protein Subcellular Localization Prediction de Shibiao Wan

Machine Learning for Protein Subcellular Localization Prediction

Autor Shibiao Wan, Man-Wai Mak
en Mixed media product – 23 apr 2015
For bioinformaticians, computational biologists, and wet-lab biologists, the authors provide the latest machine learning approaches for protein subcellular localization prediction with a systemic scheme for improving predictors performance.
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Specificații

ISBN-13: 9781501501517
ISBN-10: 1501501518
Pagini: 216
Ilustrații: Includes a print version and an ebook
Dimensiuni: 155 x 230 mm
Ediția:
Editura: De Gruyter
Locul publicării:Berlin/Boston

Notă biografică

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

Cuprins

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  ConclusionsA  Web-Servers for Protein  Subcellular Localization B  Proof of No Bias in LOOCVBibliography