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Austin Wang

@austintwang.bsky.social
195 followers 379 following 12 posts

Stanford CS PhD student working on ML/AI for genomics with @anshulkundaje.bsky.social austintwang.com

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Reposted by Austin Wang
Anshul Kundaje @anshulkundaje.bsky.social · 19/06/2025
@saramostafavi.bsky.social (@Genentech) & I (@Stanford) r excited to announce co-advised postdoc positions for candidates with deep expertise in ML for bio (especially sequence to function models, causal perturbational models & single cell models). See details below. Pls RT 1/
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Reposted by Austin Wang
Anshul Kundaje @anshulkundaje.bsky.social · 15/05/2025
Today was a big day for the lab. We had two back to back thesis defenses and the defenders defended with great science and character. Congrats to DR. Kelly Cochran & DR. @soumyakundu.bsky.social on this momentous achievement. Brilliant scientists with brilliant futures ahead. 🎉🎉🎉
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Reposted by Austin Wang
Selin Jessa @selinjessa.com · 03/05/2025
Delighted to share our latest work deciphering the landscape of chromatin accessibility and modeling the DNA sequence syntax rules underlying gene regulation during human fetal development! www.biorxiv.org/content/10.1... Read on for more: 🧵 1/16 #GeneReg 🧬🖥️
biorxiv.org
Dissecting regulatory syntax in human development with scalable multiomics and deep learning
Transcription factors (TFs) establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility, and regulating gene expre...
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Reposted by Austin Wang
Jacob Schreiber @jmschreiber91.bsky.social · 24/04/2025
Our preprint on designing and editing cis-regulatory elements using Ledidi is out! Ledidi turns *any* ML model (or set of models) into a designer of edits to DNA sequences that induce desired characteristics. Preprint: www.biorxiv.org/content/10.1... GitHub: github.com/jmschrei/led...
biorxiv.org
Programmatic design and editing of cis-regulatory elements
The development of modern genome editing tools has enabled researchers to make such edits with high precision but has left unsolved the problem of designing these edits. As a solution, we propose Ledi...
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Reposted by Austin Wang
Anshul Kundaje @anshulkundaje.bsky.social · 07/01/2025
Very excited to announce that the single cell/nuc. RNA/ATAC/multi-ome resource from ENCODE4 is now officially public. This includes raw data, processed data, annotations and pseudobulk products. Covers many human & mouse tissues. 1/ www.encodeproject.org/single-cell/...
encodeproject.org
Single cell – ENCODEHomo sapiens clickable body map
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Reposted by Austin Wang
Anshul Kundaje @anshulkundaje.bsky.social · 25/12/2024
Our ChromBPNet preprint out! www.biorxiv.org/content/10.1... Huge congrats to Anusri! This was quite a slog (for both of us) but we r very proud of this one! It is a long read but worth it IMHO. Methods r in the supp. materials. Bluetorial coming soon below 1/
biorxiv.org
ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants
Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (T...
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Reposted by Austin Wang
Arpita Singhal @arpita-s.bsky.social · 11/12/2024
Excited to announce DART-Eval, our latest work on benchmarking DNALMs! Catch us at #NeurIPS!
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Reposted by Austin Wang
amanpatel100.bsky.social @amanpatel100.bsky.social · 11/12/2024
New work! Come check out our poster tomorrow and take a look at the paper!
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Austin Wang @austintwang.bsky.social · 11/12/2024
(1/10) Excited to announce our latest work! @arpita-s.bsky.social, @amanpatel100.bsky.social , and I will be presenting DART-Eval, a rigorous suite of evals for DNA Language Models on transcriptional regulatory DNA at #NeurIPS2024. Check it out! arxiv.org/abs/2412.05430
arxiv.org
DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA
Recent advances in self-supervised models for natural language, vision, and protein sequences have inspired the development of large genomic DNA language models (DNALMs). These models aim to learn gen...
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