Nature Communications (Mar 2021)
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
- 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
Affiliations
- Claudio Durán
- Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Cluster of Excellence Physics of Life (PoL), Department of Physics, Technische Universität Dresden
- Sara Ciucci
- Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Cluster of Excellence Physics of Life (PoL), Department of Physics, Technische Universität Dresden
- Alessandra Palladini
- Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Cluster of Excellence Physics of Life (PoL), Department of Physics, Technische Universität Dresden
- Umer Z. Ijaz
- Department of Infrastructure and Environment University of Glasgow, School of Engineering
- Antonio G. Zippo
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche
- Francesco Paroni Sterbini
- Institute of Microbiology, Università Cattolica del Sacro Cuore
- Luca Masucci
- Institute of Microbiology, Università Cattolica del Sacro Cuore
- Giovanni Cammarota
- Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore
- Gianluca Ianiro
- Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore
- Pirjo Spuul
- Department of Chemistry and Biotechnology, Division of Gene Technology, Tallinn University of Technology
- Michael Schroeder
- Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden
- Stephan W. Grill
- Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden
- Bryony N. Parsons
- Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool
- D. Mark Pritchard
- Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool
- Brunella Posteraro
- Institute of Microbiology, Università Cattolica del Sacro Cuore
- Maurizio Sanguinetti
- Institute of Microbiology, Università Cattolica del Sacro Cuore
- Giovanni Gasbarrini
- Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore
- Antonio Gasbarrini
- Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore
- Carlo Vittorio Cannistraci
- Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Cluster of Excellence Physics of Life (PoL), Department of Physics, Technische Universität Dresden
- DOI
- https://doi.org/10.1038/s41467-021-22135-x
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 22
Abstract
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.
