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Produktbild: Machine Learning for Protein Subcellular Localization Prediction
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Machine Learning for Protein Subcellular Localization Prediction

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

24.04.2015

Abbildungen

58 ill., 35 tbl.

Verlag

Walter de Gruyter

Seitenzahl

192

Maße (L/B/H)

24,6/17,5/1,8 cm

Gewicht

495 g

Auflage

1

Sprache

Englisch

ISBN

978-1-5015-1048-9

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

24.04.2015

Abbildungen

58 ill., 35 tbl.

Verlag

Walter de Gruyter

Seitenzahl

192

Maße (L/B/H)

24,6/17,5/1,8 cm

Gewicht

495 g

Auflage

1

Sprache

Englisch

ISBN

978-1-5015-1048-9

Herstelleradresse

deGruyter Boston
Genthiner Straße 13
10785 Berlin
DE

Email: productsafety@degruyterbrill.com

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  • Produktbild: 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
    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