Jayoung Ryu
@JayoungR
PhD Candidate at @BIG_Harvard_PhD with @lucapinello | Bayesian modeling/ML on perturbation & single cell data
Absolutely thrilled to see the first work that I led during my PhD is finally out in @NatureGenet !! Check out the manuscript & Tweetorial 👇
We are very happy to see that our manuscript, 'Joint Genotypic and Phenotypic Outcome Modeling Improves Base Editing Variant Effect Quantification,' is now published in @NatureGenet. Read it for free here: rdcu.be/dFBBC, original twittorial below!
First pre-print from the lab! Together with @lucapinello and @danielevanbauer we developed CRISPR-CLEAR, an experimental and computational pipeline to dissect enhancers at nucleotide resolution. biorxiv.org/content/10.110…
Excited to share our work "CRISPR-CLEAR", an end-to-end workflow for dissecting enhancers at nucleotide resolution! Check out our pre-print and @DSeruggia's tweetorial below! Lead with @SWittibschlager and @zafateniac and labs @lucapinello @DSeruggia and @danielevanbauer.
First pre-print from the lab! Together with @lucapinello and @danielevanbauer we developed CRISPR-CLEAR, an experimental and computational pipeline to dissect enhancers at nucleotide resolution. biorxiv.org/content/10.110…
Excited to share our new paper on Contextual AI models for context-specific prediction in biology in @NatureMethods led by stellar @_michellemli rdcu.be/dOxQ7 Understanding how proteins work and developing new therapies requires knowing which cell types proteins act…
@MLGenX thank you for organizing a fantastic workshop at @iclr_conf and for selecting our paper on DNA-Diffusion for the Outstanding Paper Award! Congratulations to the entire team that made this possible! #ICLR2024 🎉
Thanks so much for the shoutout! 🤗 Happy to help and excited that BEAN is contributing to cool discoveries to come!
I want to give a huge shoutout to @JayoungR for being so kind and responsive in troubleshooting some issues I was having installing and running BEAN. All solved in 1 day! Also not the first time I've had a fantastic interaction with @lucapinello lab. Thanks for all of your help!
Very excited to share our study on the benchmarking of methods to detect spatially variable genes (SVGs) and peaks (SVPs), a collaborative work with @zafateniac, @SongDongyuan, @GuanaoYan, @jsb_ucla, and @lucapinello. 🧵 (1/n)
Thrilled to share our inaugural work on spatial transcriptomics where we benchmarked methods for detecting spatially variable genes and peaks. Kudos to the team @ZhijianLi3,@zafateniac, @SongDongyuan, @GuanaoYan, @jsb_ucla for this great collaboration! biorxiv.org/content/10.110….
Very excited to share our study on the benchmarking of methods to detect spatially variable genes (SVGs) and peaks (SVPs), a collaborative work with @zafateniac, @SongDongyuan, @GuanaoYan, @jsb_ucla, and @lucapinello. 🧵 (1/n)
Congratulations to @JayoungR on winning the Best Paper Award at the ICML workshop on @AI_for_Science for her internship work with us at Genentech! We will also be presenting this work next month as part of an oral presentation at MLCB in Vancouver!
How can we integrate two single-cell perturbation screens like Perturb-seq and optical pooled screens? Thrilled to introduce Perturb-OT for cross-modality matching and prediction of perturbation responses! Work with amazing @_romain_lopez_ @_bunnech & Aviv (1/)
🚀Excited to announce that I am joining @modella_ai as the Founding CTO! With @AI4Pathology @MYLu97 throughout my PhD, we have released several amazing foundation models (UNI, CONCH, and most recently PathChat published in Nature: nature.com/articles/s4158…). Now, we are going all…
🚀We’re thrilled to come out of stealth and announce PathChat 2, the 1st multimodal generative AI copilot for pathology. PathChat 2 improves upon PathChat 1 (recently published @Nature, bit.ly/3XtFSux). youtu.be/fWDU5P0ap28 Waitlist: modella.ai 🧵1/2
1/🧵Introducing Perturb-seq-in-the-loop: a sequential experimental design strategy for perturbation screens guided by multimodal priors, with 3X speedup over state-of-the-art active learning methods! With amazing @_romain_lopez_ @jchuetter Taka Kudo @antonio_science Aviv Regev
The SPECTRA package generates a series of splits with decreasing train-test similarity. Evaluating your models on these splits will give a better understanding of model generalizability. Read the preprint for more info on how this works: biorxiv.org/content/10.110…