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

Matboli, Marwa and Al-Amodi, Hiba S. and Khaled, Abdelrahman and Khaled, Radwa and Roushdy, Marian M. S. and Ali, Marwa and Diab, Gouda Ibrahim and Elnagar, Mahmoud Fawzy and Elmansy, Rasha A. and TAhmed, Hagir H. and Ahmed, Enshrah M. E. and Elzoghby, Doaa M. A. and M.Kamel, Hala F. and Farag, Mohamed F. and ELsawi, Hind A. and Farid, Laila M. and Abouelkhair, Mariam B. and Habib, Eman K. and Fikry, Heba and Saleh, Lobna A. and Aboughaleb, Ibrahim H. (2024) Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats. Frontiers in Endocrinology, 15. ISSN 1664-2392

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Abstract

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses.

Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model.

Results and 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.

Item Type: Article
Subjects: Archive Paper Guardians > Medical Science
Depositing User: Unnamed user with email support@archive.paperguardians.com
Date Deposited: 24 May 2024 13:04
Last Modified: 24 May 2024 13:04
URI: http://archives.articleproms.com/id/eprint/2823

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