Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (210 p.)

Ausgabe: 1st ed.

ISBN: 1-5015-0150-X , 1-5015-0152-6

Inhalt: 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.

Anmerkung: Description based upon print version of record. , Front matter -- , 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 , English

Weitere Ausg.: ISBN 1-5015-1048-7

Sprache: Englisch

Fachgebiete: Biologie

Schlagwort(e): Electronic books.

DOI: 10.1515/9781501501500

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Umfang: 1 online resource(xvii,192p.) : , illustrations.

Ausgabe: Electronic reproduction. Berlin/Boston : De Gruyter, 2015. Mode of access: World Wide Web.

Ausgabe: System requirements: Web browser.

Ausgabe: Access may be restricted to users at subscribing institutions.

ISBN: 9781501501500

Anmerkung: 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. , Also available in print edition. , In English.

Weitere Ausg.: ISBN 9781501510489

Weitere Ausg.: ISBN 9781501501517

Sprache: Englisch

DOI: 10.1515/9781501501500

URL: https://doi.org/10.1515/9781501501500

URL: https://doi.org/10.1515/9781501501500