Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease.
In this study, we investigate faecal microbiota composition, in an attempt to evaluate performance of classification algorithms in identifying Inflammatory Bowel Disease (IBD) and its two types: Crohn's disease (CD) and ulcerative colitis (UC). From many investigated algorithms, a random forest (RF)...
| Publicado en: | Studies in Health Technology & Informatics Vol. 285; pp. 165 - 171 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153525189&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153525189 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2021 vid: 285 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 153525189 153525189 153525189 10.3233/SHTI210591 153525189 ppf: 165 ppct: 6 formats: tig: atl: Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease. aug: au: MIHAJLOVIĆ, Andrea MLADENOVIĆ, Katarina LONČAR-TURUKALO, Tatjana BRDAR, Sanja affil: BioSense Institute, University of Novi Sad, Novi Sad, Serbia sug: subj: Machine Learning Genomics Inflammatory Bowel Diseases Diagnosis Gut Microbiota Physiology Feces Analysis Algorithms Human Colitis, Ulcerative Crohn Disease Descriptive Statistics Gut Microbiota Classification Workflow Funding Source ab: In this study, we investigate faecal microbiota composition, in an attempt to evaluate performance of classification algorithms in identifying Inflammatory Bowel Disease (IBD) and its two types: Crohn's disease (CD) and ulcerative colitis (UC). From many investigated algorithms, a random forest (RF) classifier was selected for detailed evaluation in three-class (CD versus UC versus nonIBD) classification task and two binary (nonIBD versus IBD and CD versus UC) classification tasks. We dealt with class imbalance, performed extensive parameter search, dimensionality reduction and two-level classification. In three-class classification, our best model reaches F1 score of 91% in average, which confirms the strong connection of IBD and gastrointestinal microbiome. Among most important features in three-class classification are species Staphylococcus hominis, Porphyromonas endodontalis, Slackia piriformis and genus Bacteroidetes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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