Cell lineage inference from SNP and scRNA-Seq data academic.oup.com/nar/advance-ar…

Integrating multi-omics longitudinal data to reconstruct networks underlying lung development | American Journal of Physiology-Lung Cellular and Molecular Physiology physiology.org/doi/abs/10.115…

The ever first detailed multi-omics systems biology model of temporal postnatal alveolar development is finally out! Kudos to Drs Jun Dong, Farida Ahangari, team of collaborators, AJP-Lung Cellular and Molecular Physiology & to @nih_nhlbi that funded & yes I’m an author physiology.org/doi/abs/10.115…

Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human… disq.us/t/3ijovnx

The Kotton Lab announces our new paper out today in Cell Stem Cell. Congrats to authors ⁦Killian Hurley⁩, Jun Ding, Ziv Bar-Joseph and all our collaborators! ⁦Naftali Kaminski⁩ ⁦Gustavo Mostoslavsky⁩ ⁦Wilsonlab⁩ cell.com/cell-stem-cell…

New research from Ray and Stephanie Lane Computational Biology Dept. maps regulatory and signaling networks activated following infection of cells by SARS-Cov-2: biorxiv.org/content/10.110…

Reconstructing SARS-CoV-2 response signaling and regulatory networks biorxiv.org/content/10.110…

What can we learn about #covid19 using network analysis? Evidently, a lot! Proud to share the following preprint which we've worked on with a wonderful multi-disciplinary team of collaborators, funded in part by McGill initiative in Computational Medicine . More in the thread 👇 (1/5) medrxiv.org/content/10.110…

Check out our new method to integrate single-cell data from different batches and/or sequencing platforms scCobra: Contrastive cell embedding learning with domain adaptation for single-cell data integration biorxiv.org/content/10.110…

RAMEN identifies effective indicators for severe COVID and Long COVID patients biorxiv.org/content/10.110…

🧬 Introducing scSemiProfiler: Transform large-scale single-cell studies with our cost-effective, accurate tool. Explore semi-profiling with deep generative models & active learning here: biorxiv.org/content/10.110… #SingleCell #Innovation

"Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more

Introducing scSemiProfiler! Our new tool in Nature Communications uses AI to "semi-profile" single-cell data at 1/10 to 1/3 of the cost, with near-identical results to real-profiled data. We're also building a cloud service for easy access. (nature.com/articles/s4146…)

Excited to share our deep learning tool for transposable element (TE) analysis in single-cell data: MATES! 🎯 MATES allows precise locus-specific TE quantification, offering new insights into cell dynamics through the lens of TEs. Discover more here: nature.com/articles/s4146…

1/n Happy to share our new single-cell+unsupervised AI drug discovery method is now published in Nature Biomedical Engineering!🚀 Huge thanks to Jun Ding, Naftali Kaminski and all collaborators. 🎉 We hope UNAGI can accelerate the discovery of new drugs. doi.org/10.1038/s41551…

Naftali Kaminski Jun Ding Jonas Schupp Melanie Königshoff Geremy Clair Aurelien Justet Farida Ahangari (she/her) Xiting Yan ATS G&G Section Single Cell papers Pulmonary Fibrosis News PF Warriors supports innovation in efforts to better understand the lungs and opportunities to improve them, including the responsible use of AI tools.

🚨 New in Nature Biomedical Engineering 🚨 We built virtual cells and disease models with deep generative AI—letting you simulate disease progression and test drugs in silico. Say hello to UNAGI 🧠🧬💊 @Kaminskimed Yuuuuuuuuuuuumin 🔗 nature.com/articles/s4155… #VirtualCell #AI4CureIPF

Honoured to receive the Brigadier-General Herbert Stanley Birkett Memorial Research Award from @FondationHGM. With this support, our lab at RI-MUHC will advance AI4Health: building virtual cells and virtual disease models to simulate disease and explore new therapies in silico.

.McGill University researchers have developed an artificial intelligence tool that can detect previously invisible disease markers inside single cells. 🧪 mcgill.ca/newsroom/chann… McGill Medicine and Health Sciences Quantitative Life Sciences Nature Communications

What if AI could detect disease before symptoms appear? 🧬Researchers from The Institute and McGill University unveil DOLPHIN, an AI tool paving the way for precision medicine. Centre universitaire de santé McGill McGill University McGill Medicine and Health Sciences 🔗 rimuhc.ca/en/-/new-ai-to…