Although diabetes is known to be a disease that is closely linked to genetics and epigenetics, the mechanisms underlying the onset and/or progression of the disease have sometimes not been fully addressed in order to help patients. In recent years and due to a large number of recent studies, it is known that changes in the balance of the microbiota can cause a battery of diseases. Nowadays, massive sequencing techniques allow us to obtain the metagenomic profile of an individual, whether from a part of the body, organ or tissue, thus being able to identify the composition of a given microbiota. The use of Machine Learning (ML) techniques, which do not have any biological assumptions, are capable of identifying expression patterns and relationships between characteristics. We present a model based on ML techniques and a metagenomic signature capable of stratifying patients with Type I Diabetes (TID), to serve as a support tool for clinical decision making.
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Machine Learning-based analysis of metagenomic profiles for the stratification of patients affected by type I Diabetes
Published:
23 November 2021
by MDPI
in MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
congress AI.MED-08: AI, Neuro Sciences, Med. Info., & Biomed. Eng. Congress, Coruña, Spain-Carleton, Canada-Stanford, USA, 2021
Abstract:
Keywords: machine learning; metagenomics; diabetes; microbiota; NGS
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