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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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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245 1 0 |a Machine Learning for Protein Subcellular Localization Prediction /  |c Shibiao Wan, Man-Wai Mak. 
264 1 |a Berlin ;  |a Boston :   |b De Gruyter,   |c [2015] 
264 4 |c ©2015 
300 |a 1 online resource (192 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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505 0 0 |t Frontmatter --   |t Preface --   |t Contents --   |t List of Abbreviations --   |t 1. Introduction --   |t 2. Overview of subcellular localization prediction --   |t 3. Legitimacy of using gene ontology information --   |t 4. Single-location protein subcellular localization --   |t 5. From single- to multi-location --   |t 6. Mining deeper on GO for protein subcellular localization --   |t 7. Ensemble random projection for large-scale predictions --   |t 8. Experimental setup --   |t 9. Results and analysis --   |t 10. Properties of the proposed predictors --   |t 11. Conclusions and future directions --   |t A. Webservers for protein subcellular localization --   |t B. Support vector machines --   |t C. Proof of no bias in LOOCV --   |t D. Derivatives for penalized logistic regression --   |t Bibliography --   |t Index 
506 0 |a restricted access  |u http://purl.org/coar/access_right/c_16ec  |f online access with authorization  |2 star 
520 |a 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. 
538 |a Mode of access: Internet via World Wide Web. 
546 |a In English. 
588 0 |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) 
650 0 |a Artificial intelligence  |v Congresses. 
650 0 |a Computer vision  |v Congresses. 
650 0 |a Pattern Recognition, Automated  |v Congresses. 
650 0 |a Pattern perception  |v Congresses. 
650 4 |a Bioinformatik. 
650 4 |a Informatik. 
650 4 |a Proteomik. 
650 7 |a Technology & Engineering / Signals & Signal Processing.  |2 bisacsh 
653 |a Bioinformatics. 
653 |a Computer Science. 
653 |a Proteomics. 
700 1 |a Mak, Man-Wai,   |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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773 0 8 |i Title is part of eBook package:  |d De Gruyter  |t EBOOK PACKAGE Engineering, Computer Sciences 2015  |z 9783110438772  |o ZDB-23-DEI 
776 0 |c EPUB  |z 9781501501524 
776 0 |c print  |z 9781501510489 
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