A comparison of graph- and kernel-based –omics data integration algorithms for classifying complex traits

Background High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide...

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Vydáno v:	BMC bioinformatics Ročník 18; číslo 1; s. 539 - 13
Hlavní autoři:	Yan, Kang K., Zhao, Hongyu, Pang, Herbert
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 06.12.2017 BioMed Central Ltd BMC
Témata:	Algorithms Bayes Theorem Bayesian network Bioinformatics Biomedical and Life Sciences Classification Comparative analysis Computational Biology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Graph-based semi-supervised learning Humans Hypertension Life Sciences Medical research Medicine, Experimental Microarrays Multiple data sources Relevance vector machine Research Article Semi-definite programming (SDP)-support vector machine Support Vector Machine Transcriptome analysis Graph-based semi-supervised learning Semi-definite programming (SDP)-support vector machine Multiple data sources Bayesian network Relevance vector machine Classification
ISSN:	1471-2105, 1471-2105
On-line přístup:	Získat plný text
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