Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak

• Autore: Wan Shibiao
• 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 genere / forma: Electronic books.
• 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.: 9910460442103321
• Lo trovi qui: Univ. Federico II