Yumin Zheng
@yuminzheng.bsky.social
PhD candidate @McGill | Deep learning | single cell | drug discovery
1/n Happy to share our new single-cell+unsupervised AI drug discovery method is now published in Nature Biomedical Engineering!🚀
Huge thanks to Dr. Ding, @kaminskimed.bsky.social and all collaborators. 🎉
We hope UNAGI can accelerate the discovery of new drugs.
doi.org/10.1038/s415...
#singlecell
Huge thanks to Dr. Ding, @kaminskimed.bsky.social and all collaborators. 🎉
We hope UNAGI can accelerate the discovery of new drugs.
doi.org/10.1038/s415...
#singlecell
A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases - Nature Biomedical Engineering
UNAGI deciphers cellular dynamics from human disease time-series single-cell data and facilitates in silico drug perturbations to discover drugs potentially active against complex human diseases.
doi.org
June 20, 2025 at 9:16 PM
1/n Happy to share our new single-cell+unsupervised AI drug discovery method is now published in Nature Biomedical Engineering!🚀
Huge thanks to Dr. Ding, @kaminskimed.bsky.social and all collaborators. 🎉
We hope UNAGI can accelerate the discovery of new drugs.
doi.org/10.1038/s415...
#singlecell
Huge thanks to Dr. Ding, @kaminskimed.bsky.social and all collaborators. 🎉
We hope UNAGI can accelerate the discovery of new drugs.
doi.org/10.1038/s415...
#singlecell
(1/n) Excited to share our new work accepted at ICML 2025. SUICA, a novel method to enhance the quality of spatial transcriptomics from multiple ST platforms including Visium, stereo-seq, slide-seq-V2....
#SpatialTranscriptomics
Paper at: arxiv.org/abs/2412.01124
Code at: github.com/Szym29/SUICA
#SpatialTranscriptomics
Paper at: arxiv.org/abs/2412.01124
Code at: github.com/Szym29/SUICA
May 19, 2025 at 4:31 PM
(1/n) Excited to share our new work accepted at ICML 2025. SUICA, a novel method to enhance the quality of spatial transcriptomics from multiple ST platforms including Visium, stereo-seq, slide-seq-V2....
#SpatialTranscriptomics
Paper at: arxiv.org/abs/2412.01124
Code at: github.com/Szym29/SUICA
#SpatialTranscriptomics
Paper at: arxiv.org/abs/2412.01124
Code at: github.com/Szym29/SUICA
Reposted by Yumin Zheng
A month ago we @vevotherapeutics.bsky.social announced that we have generated the largest single-cell perturbation atlas in history, Tahoe-100M. Today, we announce that we will fully open-source Tahoe-100M in Feb, as part of a collaboration with NVidia health to train cell state models.
January 13, 2025 at 4:23 PM
A month ago we @vevotherapeutics.bsky.social announced that we have generated the largest single-cell perturbation atlas in history, Tahoe-100M. Today, we announce that we will fully open-source Tahoe-100M in Feb, as part of a collaboration with NVidia health to train cell state models.
🧬Interested in quantifying the Transposable Elements expression at individual loci in your single-cell data? Check our recent work, MATES🚀
#singlecell #transposons
www.nature.com/articles/s41...
#singlecell #transposons
www.nature.com/articles/s41...
MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell - Nature Communications
Transposable elements (TEs) pose challenges for quantification due to multi-mapping reads. Here, authors present MATES, a deep learning method that accurately assigns reads to specific TE loci, enhanc...
www.nature.com
December 26, 2024 at 10:15 PM
🧬Interested in quantifying the Transposable Elements expression at individual loci in your single-cell data? Check our recent work, MATES🚀
#singlecell #transposons
www.nature.com/articles/s41...
#singlecell #transposons
www.nature.com/articles/s41...