%0 Unpublished Work %1 Ciucci2020-bd %A Ciucci, Sara %A Durán, Claudio %A Palladini, Alessandra %A Ijaz, Umer Z %A Sterbini, Francesco Paroni %A Masucci, Luca %A Cammarota, Giovanni %A Ianiro, Gianluca %A Spuul, Pirjo %A Schroeder, Michael %A Grill, Stephan W %A Parsons, Bryony N %A Pritchard, D Mark %A Posteraro, Brunella %A Sanguinetti, Maurizio %A Gasbarrini, Giovanni %A Gasbarrini, Antonio %A Cannistraci, Carlo Vittorio %D 2020 %J bioRxiv %K %T Machine learning pattern recognition and differential network analysis of gastric microbiome in the presence of proton pump inhibitor treatment or Helicobacter pylori infection %X AbstractAlthough long thought to be a sterile and inhospitable environment, the stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of orally administered drugs such as Proton Pump Inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in the early phase. They mainly focus on the most prevalent taxa and rely on linear techniques for multivariate analysis.Here we disclose the importance of complementing linear dimensionality reduction techniques such as Principal Component Analysis and Multidimensional Scaling with nonlinear approaches derived from the physics of complex systems. Then, we show the importance to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of re-organizations which emerge from combinatorial microbial variations induced by a medical treatment (PPIs) or an infectious state (H. pylori).

@unpublished{Ciucci2020-bd, abstract = {AbstractAlthough long thought to be a sterile and inhospitable environment, the stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of orally administered drugs such as Proton Pump Inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in the early phase. They mainly focus on the most prevalent taxa and rely on linear techniques for multivariate analysis.Here we disclose the importance of complementing linear dimensionality reduction techniques such as Principal Component Analysis and Multidimensional Scaling with nonlinear approaches derived from the physics of complex systems. Then, we show the importance to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of re-organizations which emerge from combinatorial microbial variations induced by a medical treatment (PPIs) or an infectious state (H. pylori).}, added-at = {2024-09-10T11:56:37.000+0200}, author = {Ciucci, Sara and Dur{\'a}n, Claudio and Palladini, Alessandra and Ijaz, Umer Z and Sterbini, Francesco Paroni and Masucci, Luca and Cammarota, Giovanni and Ianiro, Gianluca and Spuul, Pirjo and Schroeder, Michael and Grill, Stephan W and Parsons, Bryony N and Pritchard, D Mark and Posteraro, Brunella and Sanguinetti, Maurizio and Gasbarrini, Giovanni and Gasbarrini, Antonio and Cannistraci, Carlo Vittorio}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/209242357c972f7ff44a989bcb410af81/scadsfct}, institution = {bioRxiv}, interhash = {aba2e118d2b3678a4bf0481b90c7d222}, intrahash = {09242357c972f7ff44a989bcb410af81}, journal = {bioRxiv}, keywords = {}, month = mar, timestamp = {2024-09-10T15:15:57.000+0200}, title = {Machine learning pattern recognition and differential network analysis of gastric microbiome in the presence of proton pump inhibitor treatment or Helicobacter pylori infection}, year = 2020 }