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AI+ML
See, AI can be used for good ... or at the very least, a useful distraction from the bad
While OpenAI and Anthropic fight over whose models can escape their sandbox more alarmingly, Google’s DeepMind team has once again shown how machine learning can also be used to advance science for humanity’s benefit.
On Tuesday, the Chocolate Factory’s crack team of AI researchers unveiled AlphaGenome Atlas, a massive database containing a petabyte worth of data predicting the effects of nine billion possible nucleotide variations in the human genome.
According to Google, the platform, which is now publicly available to researchers, is already helping scientists to better understand the fundamentals of the human body and treat the diseases that ail it.
The database aims to address one of the bigger challenges in modern genetic research: pinpointing exactly which genetic variations are responsible for the trait or disease scientists are studying.
The database builds on DeepMind’s AlphaGenome, an AI model introduced last year, which could predict how genetic variants impact biological processes — essentially tying together cause and effect.
But while useful in targeted applications, researchers still needed to figure out which variations to test.
“By precomputing AlphaGenome’s predictions at scale, we have created an easily accessible resource that vastly expands the model's reach. Just as an atlas is a collection of maps, linking together features of the land like altitude and location, AlphaGenome Atlas charts the molecular effects of DNA variants across the genome,” the DeepMind team wrote in a blog post.
One of the key ways the database does this is by assigning each predicted variation a score referred to as its AlphaGenome Variant Impact (AVI).
This score, Google claims, can help researchers rank genetic variants by which ones are most likely to have the largest impact on the target trait, allowing them to narrow their search window instead of relying on brute force to find the needle in the haystack.
In one experiment conducted in collaboration with the GREGoR Consortium, researchers used AVI scores from the database to identify genetic variants affecting DNM1, a gene linked to epileptic encephalopathy, a rare and severe brain disorder. And perhaps more importantly, the candidates' AVI scores pointed to the mechanism by which that genetic code worked.
Specifically, DeepMind explains the genetic variant identified with the help of the database “created an incorrect splice site (a mistake in the cell’s genetic instructions) that led to an abnormal extension of the resulting protein.”
The GREGoR Consortium’s use case is just one of several spanning human genomics. However, it’s worth noting that the contents of the AlphaGenome Atlas are still predictions.
DeepMind notes that as AlphaGenome models evolve, these predictions should improve as well. As such, the platform is simply another tool in researchers’ arsenal, one that’s now openly available to poke at.
AlphaGenome and its accompanying Atlas dataset are only the latest example of how DeepMind is continuing to push the machine learning envelope beyond the frontier language models that dominate the news cycle and threaten to place the economy in a stranglehold with a chain of circular financing that may collapse if the end-user revenue doesn't show up as big as predicted. Along with AlphaFold, which is designed to predict protein structures, DeepMind has also developed several models to improve the accuracy and speed of weather forecasting.
That said, Google is playing the darker game as well, with plans to drop more than $195 billion on capital expenditures this year, while simultaneously choking independent web publishers by replacing search results with AI answers. But hey, for the greater good, right? ®
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