Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats

利用大鼠的分子和生化特征预测 2 型糖尿病治疗目标的综合机器学习模型

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作者:Marwa Matboli, Hiba S Al-Amodi, Abdelrahman Khaled, Radwa Khaled, Marian M S Roushdy, Marwa Ali, Gouda Ibrahim Diab, Mahmoud Fawzy Elnagar, Rasha A Elmansy, Hagir H TAhmed, Enshrah M E Ahmed, Doaa M A Elzoghby, Hala F M Kamel, Mohamed F Farag, Hind A ELsawi, Laila M Farid, Mariam B Abouelkhair, Eman

Discussion

Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

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