Understanding the mechanisms of aging can help us live longer and healthier lives. Epigenetic age predictors are machine learning models that use methylation levels at CpG sites to predict the biological age of the cell. Horvath’s linear clock uses 353 CpGs with a median absolute error (MedAE) of 3.530, while the deep learning model AltumAge uses 20,318 CpGs to achieve a MedAE of 2.147. This study explores how to improve the accuracy of age predictors through model architecture selection, hyperparameter optimization, and feature selection. ElasticNet regression with recursive feature elimination achieved a MedAE of 2.820 using 341 CpGs, outperforming Horvath’s clock. The two models shared 95 CpG sites, and gene enrichment analysis revealed that several associated genes are involved in stem cell regulation. Feature importance and model interpretation were performed using SHAP analysis, which indicated that age prediction cannot be captured by a small subset of CpG sites. It was concluded that epigenetics has an influence on stem cells, which was found to be a biomarker of aging. Aging remains a complex process that deep learning models may capture better.

Alzheimer’s Disease is a complex neurodegenerative disorder marked by the abnormal build-up of proteins in the brain. As no cure currently exists, understanding the disease’s cellular mechanisms is essential for advancing diagnostics and treatment. To this end, single-cell RNA sequencing (scRNA-seq) is a method that offers detailed information about the gene activity of individual cells but lacks their spatial context. Conversely, spatial transcriptomics technology preserves the localization of the cells but provides more limited transcriptomic information. To resolve this, we provide a model that predicts a cell’s distance to pathology from single-cell RNA-sequencing data. Additionally, we identify APOE, LYVE1, and SLC17A7 as genes potentially associated with AD-related microglial clustering around plaques.

Identifying key genes in Alzheimer’s Disease (AD) is important in increasing understanding about its pathogenesis, and discovering potential therapeutic targets. Recent advances in single-cell RNA sequencing (scRNAseq) technology have provided unprecedented opportunities to study the molecular mechanisms underlying AD at the cellular level. In this study, we have trained a Graph Attention Network (GAT) to predict disease status of single cells from the SEA-AD scRNAseq dataset. Furthermore, we propose a method for interpreting the learned attention weights of the GAT to score genes on their importance for the prediction, treating gene prioritization as a feature importance problem. We have identified several genes associated with AD, including RBFOX1, NRG1, NRG3, GPC6, HNRNPC and CSMD1. We also found significant gene set enrichment in several terms related to AD and dementia, warranting future research about the presented top genes.