Aging involves the gradual accumulation of damage and is a major risk factor for chronic diseases. Epigenetic mechanisms, particularly DNA methylation, play a role in aging, although the specific biological processes remain unclear. Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes need to be better understood. Despite diverse research perspectives, functional decline associated with aging remains a focal point of intense scientific interest.
Biomarkers based on DNA methylation show promise in predicting age-related changes in various DNA sources. Epigenetic clocks estimate chronological age using supervised machine learning and CpG combinations. Constructing an age estimator based on DNA methylation from multiple tissues is challenging due to differences between tissues. The Horvath clock, which employs elastic net regression at 353 CpG, is accurate across diverse DNA sources. Neural network-based methods for estimating biological age have shown high accuracy but lack interpretability, prompting the development of a biologically informed tool for interpretable predictions of prostate cancer and treatment resistance.
Researchers have proposed a deep learning prediction model called XAI-AGE (XAI stands for Explainable ai) that integrates previously identified biologically hierarchical information into a neural network model to predict biological age based on DNA methylation data. This model aligns with the hierarchy of biological pathways, similar to Elmarakeby's tool. By comparing its performance with elastic net regression, the researchers found improved prediction accuracy and highlighted the versatility of our approach. It allows evaluating the importance of CpGs, genes, biological pathways or entire branches and layers of pathways in predicting age throughout the human lifespan.
The model comprises multiple layers, each corresponding to different levels of ReactomeDB biological abstraction. CpG methylation beta values enter the input layer and information propagates through the network, connecting nodes based on shared annotations in ReactomeDB. Chronological age prediction is achieved by calculating the arithmetic mean of the results from individual layers. This approach ensures a restricted flow of information through the network, reflecting the hierarchical nature of biological pathways in ReactomeDB.
XAI-AGE outperformed first-generation predictors and matched deep learning models in accurately predicting biological age from DNA methylation. It excelled in whole blood and blood PBMC tissue types, but performed poorly in blood cord, bone marrow, and esophagus. Trained and tested on a data set of 6,547 patient samples across 54 cohorts and multiple tissues, the model integrated ReactomeDB to obtain biologically meaningful information. The model predictions made it possible to trace the flow of information and identify relevant sources.
To conclude, researchers have introduced an accurate and interpretable neural network architecture based on DNA methylation to estimate age. This model offers easy interpretation of results between tissues, age groups, and cell line differentiation. The resulting model can generate hypotheses and visualize the underlying mechanisms related to aging. The researchers have demonstrated this feature of the model by examining the importance scores of individual neurons to predict the age at which the neural network was trained on different data sets. The most notable result was probably obtained for the pan tissue data set.
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Asjad is an internal consultant at Marktechpost. He is pursuing B.tech in Mechanical Engineering at Indian Institute of technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast who is always researching applications of machine learning in healthcare.
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