gCoda: Conditional Dependence Network Inference for Compositional Data-Reference-Cited by-同舟云学术

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gCoda: Conditional Dependence Network Inference for Compositional Data

Published:2017-07 Issue:7 Volume:24 Page:699-708
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Fang Huaying¹²,Huang Chengcheng³,Zhao Hongyu⁴,Deng Minghua¹²⁵

Affiliation:

1. LMAM, School of Mathematical Sciences, Peking University, Beijing, China.

2. Center for Quantitative Biology, Peking University, Beijing, China.

3. Institute of Urban Meteorology, China Meteorological Administration, Beijing, China.

4. Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.

5. Center for Statistical Science, Peking University, Beijing, China.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

http://www.liebertpub.com/doi/pdf/10.1089/cmb.2017.0054

Reference19 articles.

1. Logistic-Normal Distributions: Some Properties and Uses

2. Investigating microbial co-occurrence patterns based on metagenomic compositional data

3. Emergence of Scaling in Random Networks

4. Learning Microbial Interaction Networks from Metagenomic Count Data

5. Extended Bayesian information criteria for model selection with large model spaces

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1. Identifying stationary microbial interaction networks based on irregularly spaced longitudinal 16S rRNA gene sequencing data;Frontiers in Microbiomes;2024-06-03

2. fastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data;Bioinformatics;2024-05-01

3. Identifying microbial drivers in biological phenotypes with a Bayesian network regression model;Ecology and Evolution;2024-05

4. CARE: Large Precision Matrix Estimation for Compositional Data;Journal of the American Statistical Association;2024-04-29

5. Correlation measures in metagenomic data: the blessing of dimensionality;2024-03-04

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