Core Viewpoint - The development of AlphaGenome by DeepMind represents a significant advancement in understanding the non-coding regions of the human genome, which were previously considered "junk DNA" but are now believed to hold crucial biological secrets [1][2]. Group 1: AlphaGenome Overview - AlphaGenome is an AI model designed to predict the effects of small changes in DNA sequences on various molecular processes, providing new pathways for decoding human gene regulation mechanisms [1][2]. - Unlike previous models that had to compromise between sequence length and prediction accuracy, AlphaGenome achieves both, capturing long-range genomic context while providing precise base-level predictions [3]. Group 2: Model Capabilities - The model can process up to 1 million base pairs at once and predict thousands of molecular properties, including gene expression, splicing patterns, protein binding sites, and chromatin accessibility across various cell types [4]. - AlphaGenome's training dataset comes from multiple large-scale public resources, and it can complete training in just 4 hours using half the computational resources of its predecessors [4]. Group 3: Applications and Limitations - The model features a variant scoring system that efficiently compares DNA sequences before and after mutations, assessing their impact across multiple biological pathways [4]. - AlphaGenome is not designed for individual genome interpretation or medical decision-making, as it currently lacks the capability to predict disease risk or ancestry information [6]. - The model's training data is limited to humans and mice, and its adaptability to other species remains unverified [6].