Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/51147
Title: Machine Learning Data Analysis Highlights the Role of Parasutterella and Alloprevotella in Autism Spectrum Disorders
Authors: Pietrucci, Daniele 
Teofani, Adelaide
Milanesi, Marco 
Fosso, Bruno
Putignani, Lorenza
Messina, Francesco
Pesole, Graziano
Desideri, Alessandro
Chillemi, Giovanni 
Journal: BIOMEDICINES 
Issue Date: 2022
Abstract: 
In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including Parasutterella and Alloprevotella. Furthermore, our results show that ML algorithms could identify common taxonomic features by comparing datasets obtained from countries characterized by latent confounding variables.
URI: http://hdl.handle.net/2067/51147
ISSN: 2227-9059
DOI: 10.3390/biomedicines10082028
Rights: CC0 1.0 Universal
Appears in Collections:A1. Articolo in rivista

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