Supervised Parametric Learning in the Identification of Composite Biomarker Signatures of Type 1 Diabetes in Integrated Parallel Multi-Omics Datasets.

Jerry BonnellOscar AlcazarBrandon Watts Peter Buchwald Midhat H AbdulredaMitsunori Ogihara

Published in: Biomedicines (2024)

the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease.

Keyphrases

type diabetes
machine learning
single cell
genome wide
rna seq
risk factors
insulin resistance
big data
gene expression
adipose tissue
dna methylation
combination therapy
deep learning
bioinformatics analysis