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[null,"1.","Record Nr.",null,"UNINA9910819391103321"]
[null,null,"Autore",null,"Wan Shibiao"]
[null,null,"Titolo",null,"Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015"]
[{"$nulls":4},"©2015"]
[null,null,"ISBN",null,"1-5015-0150-X"]
[{"$nulls":4},"1-5015-0152-6"]
[null,null,"Descrizione fisica",null,"1 online resource (210 p.)"]
[null,null,"Classificazione",null,"WC 7700"]
[null,null,"Disciplina",null,"572/.696"]
[null,null,"Soggetti",null,"Proteins - Physiological transport - Data processing"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Probabilities - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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"]
[null,null,null,"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."]