Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns

By sjmartinez • November 5, 2024

Anwar MY, Highland H, Buchanan VL, Graff M, Young K, Taylor KD, Tracy RP, Durda P, Liu Y, Johnson CW, Aguet F, Ardlie KG, Gerszten RE, Clish CB, Lange LA, Ding J, Goodarzi MO, Chen YI, Peloso GM, Guo X, Stanislawski MA, Rotter JI, Rich SS, Justice AE, Liu CT, North K. Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns. Obesity (Silver Spring). 2024 Nov;32(11):2024-2034. doi: 10.1002/oby.24137. PMID: 39497627.

Tags: Cedars Sinai DoM, Endocrinology, Publications

Related Posts

Design of Screening Cardiometabolic Opportunities Using Transformative Echocardiography Artificial Intelligence (SCOUT-Echo-AI)

Eusuff S, Kim J, Fatima Z, Yalcinkaya D, Sahashi Y, Sandhu A, Nguyen LH, Ambrosy AP, Cheng PP, Chang ST, Go AS, Ahmad FS, Cheung[...]

A Single-Cell Atlas of Diabetes-Associated Remodeling in the Human Corneal Limbus

Zha D, Wang Y, Shah R, Ebrahimi SM, Poe AJ, Amador C, Inzalaco J, Fealy A, Sawant OB, Kramerov AA, Wang ZP, Ljubimov AV, Saghizadeh[...]

Use of Myocardial Perfusion Imaging to Guide Prognosis and Management in Older Adults: Is There an Age Limit?

Patel KK, Miller RJH, Lemley M, Shanbhag A, Ramirez G, Liang JX, Acampa W, Bateman TM, Dorbala S, Einstein AJ, Hauser MT, Kaufmann PA, Feher[...]