Identification of microbiota biomarkers with orthologous gene annotation for type 2 diabetes

dc.creatorZhang, Yu-Hang
dc.creatorGuo, Wei
dc.creatorTao, Zeng
dc.creatorShiQi, Zhang
dc.creatorChen, Lei
dc.creatorGamarra, Margarita
dc.creatorMansour, Romany F.
dc.creatorEscorcia-Gutierrez, Jose
dc.creatorHuang, Tao
dc.creatorYu Dong, Cai
dc.date2021-08-19T15:34:31Z
dc.date2021-08-19T15:34:31Z
dc.date2021-07-09
dc.date.accessioned2023-10-03T19:50:41Z
dc.date.available2023-10-03T19:50:41Z
dc.identifier1664-302X
dc.identifierhttps://hdl.handle.net/11323/8555
dc.identifier10.3389/fmicb.2021.711244
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9172576
dc.descriptionType 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.
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dc.languageeng
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceFRONTIERS IN MICROBIOLOGY
dc.sourcehttps://www.frontiersin.org/articles/10.3389/fmicb.2021.711244/full
dc.subjecttype 2 diabetes
dc.subjectgut microbiome
dc.subjectmachine learning
dc.subjectfeature selection
dc.subjectsupport vector machine
dc.subjectmicrobiota biomarkers
dc.titleIdentification of microbiota biomarkers with orthologous gene annotation for type 2 diabetes
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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