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Mak / Wan

Machine Learning for Protein Subcellular Localization Prediction

Medium: Buch
ISBN: 978-1-5015-1048-9
Verlag: De Gruyter
Erscheinungstermin: 24.04.2015
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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.

Produkteigenschaften

  • Artikelnummer: 9781501510489
  • Medium: Buch
  • ISBN: 978-1-5015-1048-9
  • Verlag: De Gruyter
  • Erscheinungstermin: 24.04.2015
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2015
  • Produktform: Gebunden, HC runder Rücken kaschiert
  • Gewicht: 544 g
  • Seiten: 192
  • Format (B x H x T): 175 x 246 x 18 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

Mak, Man-Wai

Wan, Shibiao

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