Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

Journal: Diabetes, obesity & metabolism

PMID: 39370621

Abstract

AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation.

Authors

Yidong Zhu

Department of Traditional Chinese Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Jun Liu

Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
Bo Wang

Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.

Keywords

Biomarkers Diabetic Nephropathies Gene Expression Profiling Genome-Wide Association Study Humans Machine Learning Mendelian Randomization Analysis Quantitative Trait Loci Reproducibility of Results

External Resources

View on PubMed Access via DOI PubMed (39370621)

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

Abstract

Authors

Keywords

External Resources

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