Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak
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| Autore: |
Wan Shibiao
|
| Titolo: |
Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak
|
| Pubblicazione: | Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 |
| ©2015 | |
| Descrizione fisica: | 1 online resource (210 p.) |
| Disciplina: | 572/.696 |
| Soggetto topico: | Proteins - Physiological transport - Data processing |
| Machine learning | |
| Probabilities - Data processing | |
| Soggetto non controllato: | Bioinformatics |
| Computer Science | |
| Proteomics | |
| Classificazione: | WC 7700 |
| Persona (resp. second.): | MakM. W. |
| Note generali: | Description based upon print version of record. |
| Nota di bibliografia: | Includes bibliographical references and index. |
| Nota di contenuto: | 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 |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Machine learning for protein subcellular localization prediction |
| ISBN: | 1-5015-0150-X |
| 1-5015-0152-6 | |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910819391103321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |