@NovakovskyG

PhD, Illumina AI lab; interested in Deep Learning and genome regulation; also drawing, martial arts, guitar, and death metal! (he/him)

Joined January 2018
Excited to share my first contribution here at Illumina! We developed PromoterAI, a deep neural network that accurately identifies non-coding promoter variants that disrupt gene expression.🧵 (1/)
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository cell.com/cell/fulltext/S0092…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
🧬🧪🔬Why are enhancers transcribed? How does it impact gene regulation? I’m excited to share our new paper in @ScienceMagazine showing that ncRNAs control the timing of gene activation in embryos. w/Mike Levine #ScienceResearch #RNA A few highlights 🧵👇 science.org/doi/10.1126/scie…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
Excited to announce a new preprint that investigates the ability of sequence-to-function models (S2F; e.g., AlphaGenome) to identify causal expression modifying variants. (1\n) biorxiv.org/content/10.64898…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
I can not emphasize enough how much love and care has gone into this resource. Each of the 3865 models has been individually optimized and thoroughly vetted. It’s a truly foundational resource, and holds many many stories that are begging to be told.
Very excited to announce ENCODE GRAMMAR (Genomic Regulatory Atlas of sequence Models, Motifs, Annotations & Rules): 3,865 experiment-specific deep learning model sets and sequence annotations for decoding human regulatory DNA. 1/
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
[Reviewer Request] I need a few emergency reviewers for MLCB 2026. Please let me know if you have capacity to help over the next day or two. DM me with your contact email! Please RT!
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
New preprint from Shendure Lab on Locus-Scale aka Long-@$$ MPRAs led by the amazing Abby McGee & @CGBiar! Most MPRAs test ~300 bp fragments next to a promoter. But real enhancers are bigger, act combinatorially and from a distance. 1/n biorxiv.org/content/10.64898…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
Masahiro and I were fortunate to contribute some RCMC analyses to this beautiful paper from Koska and Wysocka that comprehensively dissects the determinants of promoter competition: nature.com/articles/s41588-0…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
The Encyclopedia of DNA Elements | from The ENCODE Project Consortium biorxiv.org/content/10.64898…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
Here is the recording of the talk and the paper making a case for why temporal dynamic data & sequence anchored cis-regulation will be critical to learn causal mechanistic insights into transcriptional regulation of perturbation response. 1/ nitter.cf/i/status/2075663902160…
Virtual cell enthusiasts: check out my talk today (in an hour) to understand why I think it is critical to have longitudinal (temporal) data & incorporate cis regulation into causal mechanistic models of perturbation response. Case study: fibroblast to iPSC reprogramming.
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
Today, we're excited to share that Biomni is published in @ScienceMagazine. Biomedical research is still fragmented, manual, and difficult to scale. In this work, we introduce Biomni - the first general-purpose biomedical AI agent with an integrated biology environment that can reason, plan, and execute end-to-end scientific workflows. We show that, with the right environment and harness, AI can automate large-scale omics analyses, orchestrate laboratory robotics, optimize molecular properties, and even train new AI models for biology. We also introduce a reinforcement learning recipe for continually improving biomedical AI agents, enabling open-source models to achieve frontier-level performance. It's surreal to look back. We started the Biomni project in early 2024, when agentic AI was still nascent. It is exciting to see tens of thousands of biologists collaborating with agents every day to accelerate science. Try Biomni: biomni.phylo.bio Read more: science.org/doi/10.1126/scie… This work is not possible without this truly inter-disciplinary team: @serena2z @hcwww_ @YuanhaoQ Minta Lu, Ryan Li, @yusufroohani Lin Qiu @shiyi_c98 Gavin Junze Di @rickwierenga @kavi_deniz Sherry @TianweiShe Shruti Jennefer Xin Zhou @MWheelerMD Jon Bernstein @MengdiWang10 @PengHeAtlas @zhou_jingtian @SnyderShot @lecong Aviv Regev @jure @StanfordAILab @genentech @phylo_bio @arcinstitute @UW @berkeley_ai @RetroBio_ @tamarindbio @Princeton @UCSF
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
Fantastic, meticulous expts. & analysis to answer the mysterious question: how enhancers often affect specific promoters. The answer is probably much simpler than many may have expected. Great work by @yxjtan & team. Also check out the reporter assay artifacts they discovered!
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
New study using PromoterAI to show that disruption of key transcription factor motifs reduces gene expression and further strengthens experimental results (Figure 7). biorxiv.org/content/10.64898…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
Just want to give a shout-out to David Kelley @drklly who I think often does not get the credit he deserves (outside our core community). I want to highlight why I think he is such a fantastic scientist and leader in regulatory genomics. 1/
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
This administration entire policy is to torture hard working people who actually contribute to the nation. This will lead to faster decline & push even more skilled immigrants to other nations. A special congrats to tech/biotechMAGA.
The new White House policy requiring green card applicants to apply from outside the US is a capricious attack on legal immigration. It will hurt families, leave us with fewer doctors, teachers and scientists, and hurt American competitiveness in AI.
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
Rare disease diagnoses can rely on exome sequencing, but answers may be hiding in noncoding regions. 🧬 PromoterAI is a new deep learning tool that identifies pathogenic promoter variants, which may account for up to 6% of rare disease genetic burden 🔍 science.org/doi/10.1126/scie…
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Gherman Novakovsky (слава Україні! 🇺🇦) retweeted
In a saturation MPRA of the MAPT promoter, PromoterAI tracked measured variant effects, supporting its use for prioritizing pathogenic promoter variants. biorxiv.org/content/10.64898…
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