Recent advancements in artificial intelligence for genetic research have been highlighted by two notable models: CpGPT and GenePert. CpGPT, a foundation model for DNA methylation analysis, was developed using transformer architecture and trained on over 1,500 datasets, encompassing more than 100,000 samples. This model is expected to significantly enhance the prediction of methylation patterns. Meanwhile, GenePert utilizes GenePT embeddings derived from large language models, such as GPT-4, to predict gene expression changes due to perturbations with notable accuracy. These developments represent a significant step forward in the application of AI to genomic studies, promising improved methodologies for analyzing complex biological data.