Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

Quincy A HathawaySkyler M RothMark V PintiDaniel C SprandoAmina KunovacAndrya J DurrChris C CookGarrett K FinkTristen B CheuvrontJasmine H GrossmanGhadah A AljahliAndrew D TaylorAndrew P GirominiJessica L AllenJohn M Hollander

Published in: Cardiovascular diabetology (2019)

Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discovery.

Keyphrases

machine learning
single cell
rna seq
small molecule
high throughput
current status
artificial intelligence
heart failure
atrial fibrillation