DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays | Zendy

Amrit Singh | Zendy; Casey P. Shan | Zendy; Benoît Gautier | Zendy; Florian Rohart | Zendy; Michaël Vacher | Zendy; Scott J. Tebbutt | Zendy; KimAnh Lê Cao | Zendy

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DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

Author(s) -

Amrit Singh,

Casey P. Shan,

Benoît Gautier,

Florian Rohart,

Michaël Vacher,

Scott J. Tebbutt,

KimAnh Lê Cao

Publication year - 2019

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/bty1054

Subject(s) - bioconductor , omics , computer science , computational biology , benchmark (surveying) , identification (biology) , relevance (law) , data integration , visualization , data mining , data science , bioinformatics , biology , ecology , cartography , geography , biochemistry , gene , political science , law

In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups.

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