Nonlinear Machine Learning Pattern Recognition and Bacteria-Metabolite Multilayer Network Analysis of Perturbed Gastric Microbiome

The International Society of Microbiota would like to share this paper by Carlo Vittorio Cannistraci and al. on Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

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This recent research suggests that gastric environment with its microbiota is the active gate that regulates access to the whole gastrointestinal tract, and therefore it has a remarkable impact on the correct functionality of the entire human organism. Recent studies have revealed that many orally administered drugs can perturb the elegant balance of the gastric microbiota. However, not all of them cause permanent adverse effects and particular attention should be addressed to drugs that are frequently prescribed and administered for long periods. They can cause permanent unbalance of the gastric microbiota that might generate adverse side effects for the patient’s health.

To conclude, it is necessary to include nonlinear multidimensional techniques into clinical studies based on 16 S metagenomic sequencing data, since drawing a study’s conclusions by solely relying on linear techniques, such as PCA and MDS, can lead to data misinterpretation and impair the translational path from research to diagnostic. In the era of post-genomics and systems approaches, nonlinear dimension reduction and clustering by MCE and MC-MCL can offer new insights into complex clinical 16 S metagenomics data, like the ones studied in this article or the presence of clinical sub-types, and serve as a valuable tool in the run towards precision medicine.

Full article: 

This article will be presented by Prof. Carlo Vittorio Cannistraci during the world congress on targeting microbiota 2021 which will be held on October 20-22, 2021.

Targeting Microbiota 2021 Congress
October 20-22, 2021