By integrating DNA sequence and epigenetic context, CpGPT sets new standards for predicting aging-related outcomes, offering unprecedented accuracy in assessing mortality and disease risk across various datasets. Study: CpGPT: a Foundation Model for DNA Methylation . Image Credit: Shutterstock AI *Important notice: bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
In a recent pre-print* study posted to the bioRxiv server, a team of researchers introduced the Cytosine-phosphate-Guanine Pretrained Transformer (CpGPT: a transformer-based foundation model for deoxyribonucleic acid (DNA) methylation) designed to enhance analysis and prediction across diverse tissues and conditions. Background Since the advent of transformer architecture, artificial intelligence has rapidly progressed, especially through foundation models and large language models (LLMs) that utilize self-attention to capture complex patterns. Transformers have significantly impacted biology and medicine, advancing single-cell transcriptomics and revealing previously unknown biology with models like single-cell GPT (scGPT) and Geneformer.
Despite progress in aging research, many epigenetic aging clocks still rely on simple linear models using CpG DNA methylation data, often overlooking sequence context and complex interactions. Few predictors, such a.
