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

• 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
• Physical Description: 1 online resource (192 p.)
• Other title: 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
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781501501500; 9783110700985; 9783110439687; 9783110438772
• DOI: 10.1515/9781501501500
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Shibiao Wan, Man-Wai Mak.

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.