Gherman Novakovsky (слава Україні! 🇺🇦)

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

[Gherman Novakovsky (слава Україні! 🇺🇦) @NovakovskyG]

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/)

[Kyle Farh MD PhD - Illumina AI Lab @KyleFarh]

New study using PromoterAI to show that disruption of key transcription factor motifs reduces gene expression and further strengthens experimental results (Figure 7).
https://t.co/xZyhondX7L

[Anshul Kundaje @anshulkundaje]

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/

[Anshul Kundaje @anshulkundaje]

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.

[Yale Department of Genetics @YaleGenetics]

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 🔍
https://t.co/1GoPVcAwNW

[Kyle Farh MD PhD - Illumina AI Lab @KyleFarh]

In a saturation MPRA of the MAPT promoter, PromoterAI tracked measured variant effects, supporting its use for prioritizing pathogenic promoter variants.
https://t.co/rw2eUT8Rr5

[Kyle Farh MD PhD - Illumina AI Lab @KyleFarh]

When linear models and deep learning give the same results, orders of magnitude more training data are needed https://t.co/gcjjIjWhf3 ;

[Fabian Theis @fabian_theis]

Excited to share our RegVelo paper in Cell
https://t.co/ZAnQphaXsg

We unify RNA velocity + GRNs into one model → better OOD prediction of perturbations (e.g. gene KOs), with examples incl. neural crest KO predictions 🔬

Big thanks to W Wang, Z Hu & T Sauka-Spengler 🙏

[Schübeler Lab @SchubelerLab]

Exciting new insights on CpG islands (CGIs) regulation by transcription factors (TFs)!  CGIs drive most transcription initiation with unclear regulation. We find that chromatin-opening TFs are key players—following a surprisingly simple rule.
https://t.co/mXkDodTALR
1/9

[Kishore Jaganathan @kjaganatha]

1/ PromoterAI scores are now viewable as a track in @GenomeBrowser. Below is a ClinVar example that clearly highlights the value:

[William J. Greenleaf @WJGreenleaf]

Our Human Multiomic Development Atlas paper is out in Nature today! A heart-felt "thank you" to all co-authors for their tireless work on this complex yet exciting project! Congrats all! https://t.co/iUiZz00KOt

[Jason Buenrostro @JD_Buenrostro]

It’s well known that inflammation increases cancer risk, but how?

The answer: the epigenome "remembers" inflammation and primes stem cells for cancer.

Here is our paper: https://t.co/FcnkLdpiKZ
And a special shoutout to the lead author @snaga13

A 🧵

[Peter Holderrieth @peholderrieth]

We are also releasing self-contained lecture notes that explain flow matching and diffusion models from scratch. This goes from "zero" to the state-of-the-art in modern Generative AI.

📖 Read the notes here: https://t.co/RULWDgn9pm

Joint work with @EErives40101.

[Christina Baek @_christinabaek]

Models are typically specialized to new domains by finetuning on small, high-quality datasets.

We find that repeating the same dataset 10–50× starting from pretraining leads to substantially better downstream performance, in some cases outperforming larger models. 🧵

[Anshul Kundaje @anshulkundaje]

Great to the see the flurry of single gene knockdown Perturb-seq like atlases from cell-lines, mouse brain etc over the last few days. These are undoubtedly very valuable datasets. I just want to re-iterate a few other very important expt. design considerations 1/

[Kishore Jaganathan @kjaganatha]

@SashaGusevPosts @javier_maravall Gaps *this* wide have been shown before, in Figure 2D, for splice variant effect prediction (SpliceAI is 700K parameters). The x-axis ranges from 0 to 1 here so it may not be immediately apparent, but its the same 0.6 to 0.9 gap.

[nature @Nature]

Nature research paper: Functional dissection of complex trait variants at single-nucleotide resolution

https://t.co/Rf1GsXPsCE

[Yun S. Song @yun_s_song]

Can we simulate realistic evolutionary trajectories and “replay the tape of life”? In this work, we propose a flexible, generalizable framework for modeling how the entire protein seq evolves over time while capturing complex interactions across sites. 1/n
https://t.co/DIwQ40C478

[nature @Nature]

Nature research paper: Regulatory grammar in human promoters uncovered by MPRA-based deep learning

https://t.co/XOZF9kxi1G

[Žiga Avsec @Avsecz]

AlphaGenome is out in @nature today along with model weights! 🧬

📄 Paper: https://t.co/1fHzSPiY1x
💻 Weights: https://t.co/z6JWLT4Mpv

Getting here wasn’t a straight path. We sat down @googledeepmind to discuss the story behind the model, paper & API: https://t.co/cT8CiXfnxQ

[Artem Babaian @RNA_Life]

Molecular Genetics @ University of Toronto 🇨🇦@UofT🦠 is recruiting an Assistant Professor in #Virology!!

Come build your dream lab!

🔗https://t.co/MMq1l7y1PA