Aging involves the gradual accumulation of injury and is a crucial risk factor for chronic diseases. Epigenetic mechanisms, particularly DNA methylation, play a job in aging, though the particular biological processes remain unclear. Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes have to be higher understood. Despite diverse research perspectives, the functional decline related to aging stays a point of interest of intense scientific interest.

DNA methylation-based biomarkers show promise in predicting age-related changes across various DNA sources. Epigenetic clocks estimate chronological age using supervised machine learning and CpG combos. Constructing a multi-tissue DNA methylation-based age estimator is difficult on account of tissue differences. Horvath’s clock, employing elastic net regression on 353 CpGs, is accurate across diverse DNA sources. Neural network-based methods in estimating biological age have shown high accuracy but lack interpretability, prompting the event of a biologically informed tool for interpretable predictions in prostate cancer and treatment resistance.

Researchers have proposed a deep learning prediction model named XAI-AGE (XAI stands for Explainable AI) that integrates previously identified biologically hierarchical information in a neural network model for predicting the biological age based on DNA methylation data. This model aligns with the hierarchy of biological pathways, just like Elmarakeby’s tool. Comparing its performance to elastic net regression, researchers found improved prediction precision and highlighted the flexibility of our approach. It allows for evaluating the importance of CpGs, genes, biological pathways, or entire pathway branches and layers in predicting age across the human lifespan.

The model comprises multiple layers, each corresponding to distinct levels of biological abstraction from ReactomeDB. CpG methylation beta values enter the input layer, and data propagates through the network, connecting nodes based on shared annotations in ReactomeDB. Predicting chronological age is achieved by calculating the arithmetic mean of outputs from individual layers. This approach ensures a restricted flow of knowledge through the network, reflecting the hierarchical nature of biological pathways in ReactomeDB.

XAI-AGE surpassed 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 within the blood cord, bone marrow, and esophagus. Trained and tested on a dataset of 6547 patient samples across 54 cohorts and multiple tissues, the model integrated ReactomeDB for biologically meaningful insights. The model’s predictions allowed for tracking information flow and identifying relevant sources. 

To conclude, the researchers have introduced a precise and interpretable neural network architecture based on DNA methylation for age estimation. This model offers easy result interpretation across tissues, age groups, and cell line differentiation. The resulting model can generate hypotheses and visualize the underlying mechanisms connected to aging. The researchers have demonstrated this feature of the model by examining the importance scores of the person neurons in predicting the age when the neural network was trained on different datasets. Essentially the most noteworthy result was probably obtained for the pan-tissue dataset. 

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