{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14(1)"],"submitter":["Barberio B"],"funding":["no funding associated with the work featured in this article"],"pubmed_abstract":["Ulcerative colitis (UC) is a complex immune-mediated disease in which the gut microbiota plays a central role, and may determine prognosis and disease progression. We aimed to assess whether a specific microbiota profile, as measured by a machine learning approach, can be associated with disease severity in patients with UC. In this prospective pilot study, consecutive patients with active or inactive UC and healthy controls (HCs) were enrolled. Stool samples were collected for fecal microbiota assessment analysis by 16S rRNA gene sequencing approach. A machine learning approach was used to predict the groups' separation. Thirty-six HCs and forty-six patients with UC (20 active and 26 inactive) were enrolled. Alpha diversity was significantly different between the three groups (Shannon ind"],"journal":["Gut microbes"],"pagination":["2028366"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8820804"],"repository":["biostudies-literature"],"pubmed_title":["A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach."],"pmcid":["PMC8820804"],"pubmed_authors":["Barberio B","Valle G","Ford AC","Bertazzo E","Massimi D","Savarino EV","Sattin E","Facchin S","Patuzzi I","Simionati B","Zingone F"],"additional_accession":[]},"is_claimable":false,"name":"A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach.","description":"Ulcerative colitis (UC) is a complex immune-mediated disease in which the gut microbiota plays a central role, and may determine prognosis and disease progression. We aimed to assess whether a specific microbiota profile, as measured by a machine learning approach, can be associated with disease severity in patients with UC. In this prospective pilot study, consecutive patients with active or inactive UC and healthy controls (HCs) were enrolled. Stool samples were collected for fecal microbiota assessment analysis by 16S rRNA gene sequencing approach. A machine learning approach was used to predict the groups' separation. Thirty-six HCs and forty-six patients with UC (20 active and 26 inactive) were enrolled. Alpha diversity was significantly different between the three groups (Shannon ind","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan-Dec","modification":"2025-09-01T03:08:46.809Z","creation":"2025-06-01T12:02:56.821Z"},"accession":"S-EPMC8820804","cross_references":{"pubmed":["35129058"],"doi":["10.1080/19490976.2022.2028366"]}}