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q2-sample-classifier: machine-learning tools for microbiome classification and regression
[article]
2018
bioRxiv
pre-print
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Random forest, extra trees, and gradient boosting models demonstrate the highest performance for both supervised classification and regression of microbiome data. ...
We additionally present q2-sample-classifier, a plugin for the QIIME 2 microbiome bioinformatics framework, that facilitates application of the scikit-learn classifiers to microbiome data. ...
This project was funded in part by NSF Award 1565100 to JGC, and by the Partnership for Native American Cancer Prevention (NIH/NCI U54CA143924 and U54CA143925) to JGC. ...
doi:10.1101/306167
fatcat:4gabzunohfd53bro2xk7t3b6ri
q2-sample-classifier: machine-learning tools for microbiome classification and regression
2018
Journal of Open Source Software
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q2-sample-classifier is a plugin for the QIIME 2 microbiome bioinformatics platform that facilitates access, reproducibility, and interpretation of supervised learning (SL) methods for a broad audience ...
Acknowledgments The authors thank Jai Ram Rideout for his input and assistance integrating q2-sampleclassifier with QIIME 2. ...
This work was supported by the National Science Foundation [1565100 to JGC], and by the National Institutes of Health / National Cancer Institute Partnership for Native American Cancer Prevention [U54CA143924 ...
doi:10.21105/joss.00934
pmid:31552137
pmcid:PMC6759219
fatcat:bdc6fg5hobfhzjtuvhuvrtwk3q
q2-sample-classifier: machine-learning tools for microbiome classification and regression
2018
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
q2-sample-classifier is a plugin for the QIIME 2 microbiome bioinformatics platform that facilitates access, reproducibility, and interpretation of supervised learning (SL) methods for a broad audience ...
Bokulich et al., (2018). q2-sample-classifier: machine-learning tools for microbiome classification and regression. Journal of Open Source Software, 3(30), 934. https://doi.org/10.21105/joss.00934 ...
Acknowledgments The authors thank Jai Ram Rideout for his input and assistance integrating q2-sampleclassifier with QIIME 2. ...
doi:10.3929/ethz-b-000431152
fatcat:owxqgaekxfdb7f4mxsz7l2atua
Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment
2022
PLoS Computational Biology
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Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive models. ...
Different feature classes were important for prediction of different phenotypes. ...
Dominique Barnes and Alison Goulder for the assistance with clinical sample and data collection. ...
doi:10.1371/journal.pcbi.1009876
pmid:35196323
pmcid:PMC8901057
fatcat:jlnyy2kdrvbhfo6c2xyypiztem
Predicting microbiomes through a deep latent space
2020
Bioinformatics
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Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. ...
Results Integrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance ...
Funding Research was supported by the"Severo Ochoa Program for Centres of Excellence in R&D"from the Agencia Estatal de Investigación of Spain (grant SEV-2016(grant SEV- -0672 (2017(grant SEV- -2021) to ...
doi:10.1093/bioinformatics/btaa971
pmid:33289510
pmcid:PMC8208755
fatcat:opbrnrnwmbhpbaknyevdyctkd4
Predicting microbiomes through a deep latent space
[article]
2020
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. ...
Results: Integrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance ...
Acknowledgements Thanks to the authors of Walters et al. (2018) for kindly sharing their datasets with us. ...
doi:10.1101/2020.04.27.063974
fatcat:5tcknhcydffmjntsqt6wgsumke
Multi-omics approach reveals new insights into the gut microbiome of Galleria mellonella (Lepidoptera:Pyralidae) exposed to polyethylene diet
[article]
2021
bioRxiv
pre-print
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Gut microbiome samples were processed by high throughput 16S rRNA sequencing, and Enterococcaceae and Oxalobacteraceae were found to be the major bacterial families. ...
At low polyethylene dose, we detect no bacterial community change and no amplicon sequence variant associated with the polyethylene diet suggesting microbiome resilience. ...
Bokulich NA, Dillon M, Evan B, et al (2018) q2-sample-classifier: machine-learning tools for
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microbiome classification and regression. 5485:
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25. ...
doi:10.1101/2021.06.04.446152
fatcat:wuznuf5jgrgmhkq6zksfiitaum
QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data
2020
Current Protocols in Bioinformatics
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We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses-e.g., improving taxonomic classification ...
For more information about QIIME 2, see https://qiime2.org. ...
The QIIME 2 plugin q2-sample-classifier ; https:// library.qiime2.org/ plugins/ q2-sample-classifier/ ) contains methods for performing supervised classification/regression and feature selection using ...
doi:10.1002/cpbi.100
pmid:32343490
fatcat:i2ssiyaxvbartoaarja5p7f5dq
Non-Celiac Gluten/Wheat Sensitivity: Clinical Characteristics and Microbiota and Mycobiota Composition by Response to the Gluten Challenge Test
2021
Nutrients
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For Bacteroides (p = 0.05) and Parabacteroides (p = 0.007), the frequency of amplicon sequence variants was lower, and that for Blautia (p = 0.009) and Streptococcus (p = 0.004) was higher in NCGS individuals ...
at multiple regression analyses. ...
Acknowledgments: We are grateful to Lucia Fransos, Martina Oddenino, Silvia Marcolin, Lucia Ferraris, and Sara Pellegrino for their precious contribution to this study. ...
doi:10.3390/nu13041260
pmid:33921293
fatcat:wx7cbfeupvgvbdd3737y5kkqdy