Artificial intelligence is helping scientists read gene behavior more like language, revealing how genes cluster, shift roles, and shape disease. A new model from Mount Sinai learns from vast datasets to predict missing links, spotlight obscure genes, and hint at faster biomedical discoveries. Artificial intelligence has transformed how computers understand human language. Now, scientists at the Icahn School of Medicine at Mount Sinai are using a similar idea to decode one of biology’s biggest mysteries: how genes work together inside human cells. In a new study, researchers introduced a gene set foundation model, or GSFM, designed to learn relationships between genes across millions of biological datasets. The system draws inspiration from large language models such as ChatGPT, which learn how words gain meaning from context. Instead of studying sentences, however, this new AI studies groups of genes. The result is a system that can predict how genes interact, identify poorly understood genes and even suggest possible disease targets. Researchers believe the model could eventually improve drug discovery, diagnostics and the understanding of human disease. “Genes …