Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Nonparametric Regularized Regression for Phenotype-Associated Taxa Selection and Network Construction with Metagenomic Count Data.

Journal of computational biology : a journal of computational molecular cell biology - 1 Nov 2016

Guo Wenchuan, Liu Zhenqiu, Ma Shujie

Abstract excerpt

We use a metagenomic approach and network analysis to investigate the relationships between phenotypes across taxa under different environmental conditions. The network structure of taxa can be affected by the disease-associated environmental conditions. In addition, taxa abundance is differentiated under conditions. Therefore, knowing how the correlation or relative abundance changes with these factors would be...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Bacteria

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computational Biology

Share to Metagenomics

Share to Phenotype

Share to Regression Analysis

Source

Identifiers

PMID: 27427793
DOI: 10.1089/cmb.2016.0023

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Nonparametric Regularized Regression for Phenotype-Associated Taxa Selection and Network Construction with Metagenomic Count Data.PMID 27427793Read ↗

Multilevel regularized regression for simultaneous taxa selection and network construction with metagenomic count data.Semantic proximity Read ↗

Correlating High-dimensional longitudinal microbial features with time-varying outcomes with FLORALSemantic proximity Read ↗

Identifying Effect Modification of Latent Population Characteristics on Risk Factors with a Sparse Varying Coefficient RegressionSemantic proximity Read ↗

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.Semantic proximity Read ↗

PGS: a tool for association study of high-dimensional microRNA expression data with repeated measures.Semantic proximity Read ↗

Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence dataSemantic proximity Read ↗

Transformed low-rank ANOVA models for high-dimensional variable selection.Semantic proximity Read ↗

Negative Binomial factor regression with application to microbiome data analysisSemantic proximity Read ↗

ZERO-INFLATED NEGATIVE BINOMIAL REGRESSION FOR DIFFERENTIAL ABUNDANCE TESTING IN MICROBIOME STUDIESSemantic proximity Read ↗

Detecting Sparse Microbial Association Signals Adaptively From Longitudinal Microbiome Data Based on Generalized Estimating EquationsSemantic proximity Read ↗

A Comparison of Two-Stage Approaches Based on Penalized Regression for Estimating Gene Networks.Semantic proximity Read ↗

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.