Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
Drug use or bacterial infection can cause significant alterations of gastric microbiome. Here, the authors show how advanced pattern recognition by nonlinear machine intelligence can help disclose a bacteria-metabolite network which enlightens mechanisms behind such perturbations.
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Nature Portfolio
2021
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oai:doaj.org-article:1a4e3dda819d41df95b04fd64ec82e282021-12-02T13:24:22ZNonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome10.1038/s41467-021-22135-x2041-1723https://doaj.org/article/1a4e3dda819d41df95b04fd64ec82e282021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22135-xhttps://doaj.org/toc/2041-1723Drug use or bacterial infection can cause significant alterations of gastric microbiome. Here, the authors show how advanced pattern recognition by nonlinear machine intelligence can help disclose a bacteria-metabolite network which enlightens mechanisms behind such perturbations.Claudio DuránSara CiucciAlessandra PalladiniUmer Z. IjazAntonio G. ZippoFrancesco Paroni SterbiniLuca MasucciGiovanni CammarotaGianluca IaniroPirjo SpuulMichael SchroederStephan W. GrillBryony N. ParsonsD. Mark PritchardBrunella PosteraroMaurizio SanguinettiGiovanni GasbarriniAntonio GasbarriniCarlo Vittorio CannistraciNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-22 (2021) |
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Science Q Claudio Durán Sara Ciucci Alessandra Palladini Umer Z. Ijaz Antonio G. Zippo Francesco Paroni Sterbini Luca Masucci Giovanni Cammarota Gianluca Ianiro Pirjo Spuul Michael Schroeder Stephan W. Grill Bryony N. Parsons D. Mark Pritchard Brunella Posteraro Maurizio Sanguinetti Giovanni Gasbarrini Antonio Gasbarrini Carlo Vittorio Cannistraci Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome |
description |
Drug use or bacterial infection can cause significant alterations of gastric microbiome. Here, the authors show how advanced pattern recognition by nonlinear machine intelligence can help disclose a bacteria-metabolite network which enlightens mechanisms behind such perturbations. |
format |
article |
author |
Claudio Durán Sara Ciucci Alessandra Palladini Umer Z. Ijaz Antonio G. Zippo Francesco Paroni Sterbini Luca Masucci Giovanni Cammarota Gianluca Ianiro Pirjo Spuul Michael Schroeder Stephan W. Grill Bryony N. Parsons D. Mark Pritchard Brunella Posteraro Maurizio Sanguinetti Giovanni Gasbarrini Antonio Gasbarrini Carlo Vittorio Cannistraci |
author_facet |
Claudio Durán Sara Ciucci Alessandra Palladini Umer Z. Ijaz Antonio G. Zippo Francesco Paroni Sterbini Luca Masucci Giovanni Cammarota Gianluca Ianiro Pirjo Spuul Michael Schroeder Stephan W. Grill Bryony N. Parsons D. Mark Pritchard Brunella Posteraro Maurizio Sanguinetti Giovanni Gasbarrini Antonio Gasbarrini Carlo Vittorio Cannistraci |
author_sort |
Claudio Durán |
title |
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome |
title_short |
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome |
title_full |
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome |
title_fullStr |
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome |
title_full_unstemmed |
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome |
title_sort |
nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/1a4e3dda819d41df95b04fd64ec82e28 |
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