Machine Learning for Protein Subcellular Localization Prediction /

Machine Learning for Protein Subcellular Localization Prediction / / Shibiao Wan, Man-Wai Mak.

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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 dimens...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Plus eBook-Package 2015
VerfasserIn:
Wan, Shibiao,
Mak, Man-Wai,
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2015]
©2015
Year of Publication:2015
Language:English
Online Access:
Physical Description:1 online resource (192 p.)
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Table of Contents:
  • Frontmatter
  • Preface
  • Contents
  • List of Abbreviations
  • 1. Introduction
  • 2. Overview of subcellular localization prediction
  • 3. Legitimacy of using gene ontology information
  • 4. Single-location protein subcellular localization
  • 5. From single- to multi-location
  • 6. Mining deeper on GO for protein subcellular localization
  • 7. Ensemble random projection for large-scale predictions
  • 8. Experimental setup
  • 9. Results and analysis
  • 10. Properties of the proposed predictors
  • 11. Conclusions and future directions
  • A. Webservers for protein subcellular localization
  • B. Support vector machines
  • C. Proof of no bias in LOOCV
  • D. Derivatives for penalized logistic regression
  • Bibliography
  • Index

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