Ensemble positive unlabeled learning for disease gene identification

10.1371/journal.pone.0097079

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Bibliographic Details
Main Authors:	Yang P., Li X., Chua H.-N., Kwoh C.-K., Ng S.-K.
Other Authors:	DEPT OF COMPUTER SCIENCE
Format:	Article
Published:	2019
Subjects:	article cardiovascular disease classification algorithm comparative study endocrine disease ensemble positive unlabeled learning eye disease gene expression gene identification gene ontology genetic similarity genotype phenotype correlation learning algorithm machine learning measurement accuracy metabolic disorder neoplasm neurologic disease prediction protein interaction sensitivity analysis algorithm artificial intelligence biological model biology evaluation study gene regulatory network genetic association genetic disorder genetics human phenotype procedures selection bias trends Algorithms Artificial Intelligence Computational Biology Gene Ontology Gene Regulatory Networks Genetic Association Studies Genetic Diseases, Inborn Humans Models, Genetic Phenotype Selection Bias
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/161412
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Institution:	National University of Singapore

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spelling	sg-nus-scholar.10635-1614122023-11-01T07:35:28Z Ensemble positive unlabeled learning for disease gene identification Yang P. Li X. Chua H.-N. Kwoh C.-K. Ng S.-K. DEPT OF COMPUTER SCIENCE article cardiovascular disease classification algorithm comparative study endocrine disease ensemble positive unlabeled learning eye disease gene expression gene identification gene ontology genetic similarity genotype phenotype correlation learning algorithm machine learning measurement accuracy metabolic disorder neoplasm neurologic disease prediction protein interaction sensitivity analysis algorithm artificial intelligence biological model biology evaluation study gene regulatory network genetic association genetic disorder genetics human phenotype procedures selection bias trends Algorithms Artificial Intelligence Computational Biology Gene Ontology Gene Regulatory Networks Genetic Association Studies Genetic Diseases, Inborn Humans Models, Genetic Phenotype Selection Bias 10.1371/journal.pone.0097079 PLoS ONE 9 5 e97079 2019-11-05T00:37:28Z 2019-11-05T00:37:28Z 2014 Article Yang P., Li X., Chua H.-N., Kwoh C.-K., Ng S.-K. (2014). Ensemble positive unlabeled learning for disease gene identification. PLoS ONE 9 (5) : e97079. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0097079 1932-6203 https://scholarbank.nus.edu.sg/handle/10635/161412 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ Unpaywall 20191101
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	article cardiovascular disease classification algorithm comparative study endocrine disease ensemble positive unlabeled learning eye disease gene expression gene identification gene ontology genetic similarity genotype phenotype correlation learning algorithm machine learning measurement accuracy metabolic disorder neoplasm neurologic disease prediction protein interaction sensitivity analysis algorithm artificial intelligence biological model biology evaluation study gene regulatory network genetic association genetic disorder genetics human phenotype procedures selection bias trends Algorithms Artificial Intelligence Computational Biology Gene Ontology Gene Regulatory Networks Genetic Association Studies Genetic Diseases, Inborn Humans Models, Genetic Phenotype Selection Bias
spellingShingle	article cardiovascular disease classification algorithm comparative study endocrine disease ensemble positive unlabeled learning eye disease gene expression gene identification gene ontology genetic similarity genotype phenotype correlation learning algorithm machine learning measurement accuracy metabolic disorder neoplasm neurologic disease prediction protein interaction sensitivity analysis algorithm artificial intelligence biological model biology evaluation study gene regulatory network genetic association genetic disorder genetics human phenotype procedures selection bias trends Algorithms Artificial Intelligence Computational Biology Gene Ontology Gene Regulatory Networks Genetic Association Studies Genetic Diseases, Inborn Humans Models, Genetic Phenotype Selection Bias Yang P. Li X. Chua H.-N. Kwoh C.-K. Ng S.-K. Ensemble positive unlabeled learning for disease gene identification
description	10.1371/journal.pone.0097079
author2	DEPT OF COMPUTER SCIENCE
author_facet	DEPT OF COMPUTER SCIENCE Yang P. Li X. Chua H.-N. Kwoh C.-K. Ng S.-K.
format	Article
author	Yang P. Li X. Chua H.-N. Kwoh C.-K. Ng S.-K.
author_sort	Yang P.
title	Ensemble positive unlabeled learning for disease gene identification
title_short	Ensemble positive unlabeled learning for disease gene identification
title_full	Ensemble positive unlabeled learning for disease gene identification
title_fullStr	Ensemble positive unlabeled learning for disease gene identification
title_full_unstemmed	Ensemble positive unlabeled learning for disease gene identification
title_sort	ensemble positive unlabeled learning for disease gene identification
publishDate	2019
url	https://scholarbank.nus.edu.sg/handle/10635/161412
_version_	1781791753113698304

Ensemble positive unlabeled learning for disease gene identification

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