Brodsky, S. (2026, February 10). AI models at IBM and DeepMind are pushing DNA toward a GPT era. IBM Think.
https://www.ibm.com/think/news/ibm-deepmind-ai-models-pushing-dna-toward-gpt-era
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“What I found most novel about AlphaGenome was its multimodal nature,” Mark Gerstein, the Albert L Williams Professor of Biomedical Informatics at Yale University, and who was not involved in the research, told IBM Think in an interview. “The fact that it is trained on data from many different genomic modalities—for instance, RNA-seq, ATAC-seq and Hi-C—and predicts effects across these modalities is particularly notable.”
Gerstein said AlphaGenome stands out because it tries to predict multiple genomic signals simultaneously and treats them as connected rather than independent. Changes in chromatin state upstream, for example, can shape gene expression downstream—and models have long recognized those links. What’s new, in his view, is the scale at which AlphaGenome tries to fold those relationships directly into
sequence-to-function prediction.
He also highlighted how much DNA the model can “see” in one pass. The window, he said, is unusually large, on the order of a megabase. It’s a span big enough to capture regulatory effects that can sit far from the genes they influence.
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Gerstein’s reaction comes with an asterisk: he called the results promising, but stressed that performance on curated benchmarks doesn’t always translate to messy real-world biology.
AlphaGenome, as he sees it, is powerful at describing what a single change might do within a genome model. But real genomes do not change one letter at a time. They come as whole, inherited packages, full of variants that shape one another’s effects. “In terms of limitations, one major issue is that the model predicts the effect of only a single variant and does not take into account the full genetic background of an individual’s personal genome,” he said. “Background genetics can substantially influence the impact of a particular variant,
particularly by strongly affecting how a gene is expressed in response to a mutation.”
He thinks the next step is imaginable, even if it is harder: a future version of this kind of work could move beyond scoring a single mutation in isolation and instead operate directly on personal genomes. “One could imagine extending AlphaGenome by building large models that operate directly on personal genomes,” he said.
Medicine demands forms of evidence that many model developers simply do not have access to, Gerstein noted.
“With respect to translation into clinical practice, the main requirement is the accumulation of many use cases in which the effects of particular mutations are documented, followed by downstream validation showing that the predictions are accurate and clinically useful,” Gerstein said. “There is no substitute in the medical world for experimental data and actual clinical validation, and this will be necessary before outputs from tools like this are accepted.” ….
He also stressed what AlphaGenome does not claim to do: “It is important to remember that this tool provides the molecular
consequences of specific mutations, not downstream phenotypic or disease-level effects,” he said. “As a result, additional work would be required to bridge that gap.”
“With respect to translation into clinical practice, the main requirement is the accumulation of many use cases in which the effects of particular mutations are documented, followed by downstream validation showing that the predictions are accurate and clinically useful,” Gerstein said. “In the medical world, there is no substitute for experimental data and actual clinical validation.”
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