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Jun Cheng

@s6juncheng

Computational Biology | Machine learning research scientist at @GoogleDeepMind
Joined December 2016
396 Following 2.5K Followers 163 Posts

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Jun Cheng @s6juncheng
Prioritizing and interpreting disease-associated genetic variants remains one of the greatest challenges in human genetics. Today, we’re thrilled to introduce AlphaGenome Atlas 🧬, a genome-wide platform providing precomputed predictions for the regulatory effects of all ~9 billion possible single-letter changes and >100M observed indels in the human genome.

Here is what Atlas delivers:

1. Variant Prioritization via AVI
To prioritize variants, we developed the AlphaGenome Variant Impact (AVI) score. AVI predicts a unified score per variant, where higher values indicate greater disruption. It achieves state-of-the-art performance across diverse benchmarks. As a proof-of-concept with our collaborators at @broadinstitute, AVI prioritized a deep-intronic variant in DNM1, helping solve a previously unexplained rare epileptic encephalopathy case by revealing a brain-specific cryptic splice site.  

2. Multi-Layer Molecular Interpretation
Variant prioritization is only half the battle; researchers also need to understand why a variant matters. Atlas decomposes variant effects across multiple interpretable layers:
•Feature Attributions which decompose each variant’s score into specific biological modalities driving the impact. 
•Cell-Type Specificity: Precomputed predictions with AlphaGenome across hundreds of biosamples reveal the exact cellular context in which a variant acts. 
•Regulatory Grammar: Over 2,600 de novo DNA motifs (and >250B genome-wide instances) show when variants directly disrupt critical regulatory binding "words".  

Explore the resource:
🌐 Interactive browser & precomputed data: alphagenome.google/atlas
- 🎥 Video: youtu.be/U0aToL5C-bQ
- 📖 Blog: goo.gle/4heuCvn
- 📄 Preprint: storage.googleapis.com/deepmind-media…
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Jun Cheng @s6juncheng
We are on top for Biology, lots of hard work paid off! newsletter.semianalysis.com/i/223102484/bi…
newsletter.semianalysis.com GDPx: Measuring Frontier Model Capabilities Across the Most Valuable Domains A frontier benchmark spanning RSI, hardware design, cyber, bio, finance, legal, quant trading, chip design, software engineering, materials science, and alignment
SemiAnalysis Verified account @SemiAnalysis_ Nov 2
Today we’re releasing GDPx, a benchmark for measuring agentic capabilities across the most valuable domains.

GDPx is a true frontier-level benchmark: even the best model scores just 12% (1/7)🧵
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Jun Cheng @s6juncheng
We are offering 10,000 scientists the highest usage tier in Google Antigravity free of charge for 6 months. Please apply! forms.gle/WvmBbPvRbpKG1B…
docs.google.com Interest form: Accelerate scientific workflows in Google Antigravity 📣 Update (November 10, 2026): We’re expanding access to Gemini 4 Carbon for current and future participants, and going forward, the program will continue providing access to our latest frontier...
Google DeepMind @GoogleDeepMind Oct 13
Gemini 4 Argon is now globally available.

Built to solve your hardest problems from start to finish, the model brings frontier capabilities across coding, enterprise workflows, and cyber defense.
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Jun Cheng @s6juncheng
Great to see this, congrats!
Alex Rives Verified account @alexrives Apr 29
Scaling laws are powering AI. It’s time to scale biology.

Today we’re launching the Virtual Biology Initiative to generate the data to unlock scaling laws in biology and build accurate predictive models of the cell.

Digital representations of proteins are already expanding our understanding of life at the molecular level, and accelerating the design of molecules and medicines. Accurate digital representations of the cell could reveal the mechanisms that are responsible for disease, and show how to reverse them.

The protein data bank, and worldwide repositories of protein sequence biodiversity were created through decades of work by the scientific community. The advances in artificial intelligence for proteins would not have been possible without them.

The cell is orders of magnitude more complex, and we will need to create the data in just a few years rather than decades.

This will require a coordinated global effort. We're partnering with Broad, Wellcome Sanger, Arc, Allen, Human Cell Atlas, Human Protein Atlas, NVIDIA, and Renaissance Philanthropy.

Biohub is contributing to this effort as both a funder and a builder. We are developing microscopy to observe millions of cells in living organisms, and cryo-ET to resolve the cell in atomic detail. We're building instruments that expand the range of modalities and parameters that can be simultaneously measured. We’re developing molecular, cellular, and tissue engineering to create models of disease and design interventions.

The data we generate will be available to the worldwide scientific community.

We’re also committing $100M over the next five years to support work beyond Biohub.

We invite other scientific teams and funders to join.

Link: biohub.org/news/virtual-b…
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