Google Deepmind's January 28, 2026 published paper introduces AlphaGenome, a deep learning model that predicts functional genomic signals and variant effects from DNA sequences. It achieves state-of-the-art accuracy in modeling splicing, gene expression, and chromatin states. It enables unified, multimodal analysis of genetic variation. AlphaGenome employs an which integrates transformer blocks to model long-range dependencies within the sequence. Furthermore, the model utilizes knowledge distillation during a second training phase, where a single student model learns to reproduce the predictions of an ensemble of teacher models. The AlphaGenome paper explicitly cites "Uncertainty-aware genomic deep learning with knowledge distillation" (Zhou et al., 2024) as the basis for its distillation approach. Source: January 28 2026, Advancing regulatory variant effect prediction with AlphaGenome Google DeepMind Žiga Avsec, Natasha Latysheva, Jun Cheng, Guido Novati, Kyle R. Taylor, Tom Ward, Clare Bycroft, Lauren Nicolaisen, Eirini Arvaniti, Joshua Pan, Raina Thomas, Vincent Dutordoir, Matteo Perino, Soham De, Alexander Karollus, Adam Gayoso, Toby Sargeant, Anne Mottram, Lai Hong Wong, Pavol Drotár, Adam Kosiorek, Andrew Senior, Richard Tanburn, Taylor Applebaum, Souradeep Basu, Demis Hassabis, Pushmeet Kohli, https://doi.org/10.1038/s41586-025-10014-0
