ArticleInternational journal of molecular sciences2025
Research on Prediction of Multiple Degenerative Diseases and Biomarker Screening Based on DNA Methylation.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
The aging process will lead to a gradual functional decline in the human body, and even accelerate a significantly increased risk of degenerative diseases. DNA methylation patterns change markedly with one's age, serving as a biomarker of biological age and closely linked to the occurrence and progression of age-related diseases. Currently, diagnostic methods for individual degenerative diseases are relatively mature. However, aging often accompanies the onset of multiple degenerative diseases, presenting certain limitations in existing diagnostic models. Additionally, some identified DNA methylation biomarkers are typically applicable to only one or a few types of cancer or diseases, further restricting their utility. We endeavor to screen for biomarkers associated with multiple degenerative diseases from the perspective of aging-related co-morbid mechanisms and to perform multiple degenerative disease diagnoses. In this study, we explored research based on methylation correlations and patterns to investigate shared mechanisms across multiple degenerative diseases, identifying a set of biomarkers associated with them. We validated these biomarkers with biological omics analysis and the prediction of multiple classes of degenerative diseases, screened the biomarkers from 600 to 110 by biological omics analysis, and demonstrated the validity and predictive ability of the screened 110 biomarkers. We propose a disease diagnostic model based on a multi-scale one-dimensional convolutional neural network (MSDCNN) and a multi-class degenerative disease prediction model (ResDegNet). The two models are well trained and tested to accurately diagnose diseases and categorize four types of degenerative diseases. The research identified 110 biomarkers associated with degenerative diseases, providing a foundation for further exploration of age-related degenerative conditions. This work aims to facilitate early diagnosis, the identification of biomarkers, and the development of therapeutic targets for drug interventions.
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