Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data | Zendy

Tanya P. Garcia | Zendy; Samuel Müller | Zendy; Raymond J. Carroll | Zendy; Rosemary L. Walzem | Zendy

Open Access

Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data

Author(s) -

Tanya P. Garcia,

Samuel Müller,

Raymond J. Carroll,

Rosemary L. Walzem

Publication year - 2013

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/btt608

Subject(s) - identification (biology) , microbiome , gut microbiome , regularization (linguistics) , computational biology , computer science , statistics , artificial intelligence , biology , mathematics , bioinformatics , ecology

Gut microbiota can be classified at multiple taxonomy levels. Strategies to use changes in microbiota composition to effect health improvements require knowing at which taxonomy level interventions should be aimed. Identifying these important levels is difficult, however, because most statistical methods only consider when the microbiota are classified at one taxonomy level, not multiple.

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