The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
Network analysis methods for studying microbial communities: A mini review
Open accessAbstract
Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Statistical Methods for Research Workers
(1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,880
Enterotypes of the human gut microbiome
MetaHIT Consortium (additional members), Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D. R., Fernandes, G. R., Tap, J., Bruls, T., et al. (2011) · Nature · doi:10.1038/nature09944 · Cited by 7,548
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355
Microbial interactions: from networks to models
Faust, K., & Raes, J. (2012) · Nature Reviews Microbiology · doi:10.1038/nrmicro2832 · Cited by 4,141
Microbial diversity in the deep sea and the underexplored “rare biosphere”
Sogin, M. L., Morrison, H. G., Huber, J. A., Welch, D. M., Huse, S. M., Neal, P. R., Arrieta, J. M., & Herndl, G. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605127103 · Cited by 3,701
The Statistical Analysis of Compositional Data
Aitchison, J. (1982) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1982.tb01195.x · Cited by 3,405
Molecular ecological network analyses
Deng, Y., Jiang, Y.-H., Yang, Y., He, Z., Luo, F., & Zhou, J. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-113 · Cited by 3,196
Newman, M. E. J. (2003) · Physical Review E · doi:10.1103/physreve.67.026126 · Cited by 3,087
Microbiome Datasets Are Compositional: And This Is Not Optional
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., & Egozcue, J. J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02224 · Cited by 3,044 · cited 3×
“Firstly, microbiome data are compositional [11]; i.e. microbial counts represent proportions instead of absolute abundances.”
BackgroundInfluential
Cited by
Park, S., Zhang, T., Yue, Y., & Wu, X. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23115935 · Cited by 47
AI in microbiome‐related healthcare
Probul, N., Huang, Z., Saak, C. C., Baumbach, J., & List, M. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.70027 · Cited by 36
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Metadata
- Authors
- Matchado, M. S., Lauber, M., Reitmeier, S., Kacprowski, T., Baumbach, J., Haller, D., & List, M.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2021
- Volume
- 19
- Pages
- 2687-2698
- ISSN
- 2001-0370 (ISSN-L)
- OpenAlex
- W3158506552
- Cited by
- 357
- References
- 121
- FWCI
- 24.98
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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