DNA sequence variant effect prediction model operating at single-cell-type and state resolution with cross-modal imputation.
Google DeepMind has released AlphaGenome 2, shifting DNA variant effect prediction from tissue-level averages to individual cell types and states, identifying nearly twice as many expression-altering variants as the previous best model. Built on a conditional architecture trained on over 107 million single-cell profiles with cross-modal imputation support, it is now available via API for non-commercial research.
DeepMind has announced AlphaGenome 2, a next-generation DNA sequence model that predicts how DNA sequences and their variants affect hundreds of specific human cell types, bridging single-cell biology and human genetics. The model offers scientists a powerful tool to uncover the cellular drivers of disease and accelerate the path from genetic variants to disease mechanisms.
Google DeepMind has released AlphaGenome 2, a successor to its genome-predicting model that forecasts how genetic variants affect individual cell types and states. The model targets the challenge that the same variant can have divergent effects across cells, offering finer resolution for disease mechanism research and therapeutic discovery.