Unsupervised multiple kernel learning for heterogeneous data integration | Zendy

Jérôme Mariette | Zendy; Nathalie VillaVialaneix | Zendy

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Unsupervised multiple kernel learning for heterogeneous data integration

Author(s) -

Jérôme Mariette,

Nathalie VillaVialaneix

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx682

Subject(s) - computer science , interpretability , kernel (algebra) , data mining , machine learning , data integration , kernel method , multiple kernel learning , metagenomics , artificial intelligence , data science , biology , support vector machine , mathematics , biochemistry , combinatorics , gene

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.

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