Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data | Zendy

Qunyuan Zhang | Zendy; Haley Abel | Zendy; Alan Wells | Zendy; Petra Lenzini | Zendy; Felicia Gomez | Zendy; Michael A. Province | Zendy; Alan R. Templeton | Zendy; George M. Weinstock | Zendy; Nita H. Salzman | Zendy; Ingrid B. Borecki | Zendy

Open Access

Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data

Author(s) -

Qunyuan Zhang,

Haley Abel,

Alan Wells,

Petra Lenzini,

Felicia Gomez,

Michael A. Province,

Alan R. Templeton,

George M. Weinstock,

Nita H. Salzman,

Ingrid B. Borecki

Publication year - 2015

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/btu855

Subject(s) - lasso (programming language) , feature selection , microbiome , computer science , cart , artificial intelligence , random forest , regression , regression analysis , selection (genetic algorithm) , data mining , machine learning , statistics , biology , bioinformatics , mathematics , mechanical engineering , world wide web , engineering

Establishment of a statistical association between microbiome features and clinical outcomes is of growing interest because of the potential for yielding insights into biological mechanisms and pathogenesis. Extracting microbiome features that are relevant for a disease is challenging and existing variable selection methods are limited due to large number of risk factor variables from microbiome sequence data and their complex biological structure.

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