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Record Nr. |
UNINA9910797139603321 |
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Autore |
Wan Shibiao |
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Titolo |
Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak |
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Pubbl/distr/stampa |
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Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 |
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©2015 |
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ISBN |
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1-5015-0150-X |
1-5015-0152-6 |
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Descrizione fisica |
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1 online resource (210 p.) |
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Classificazione |
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Disciplina |
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Soggetti |
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Proteins - Physiological transport - Data processing |
Machine learning |
Probabilities - Data processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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Sommario/riassunto |
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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 dimensionality reduction. |
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