Jun Ding
@johnding86
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Assistant Professor @ Meakins-Christie Laboratories, McGill University
Montreal, QC
Joined March 2018
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. @cusm_muhc @mcgillu @McGillMed 🔗 https://t.co/aeTWFS2431
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.@mcgillu researchers have developed an artificial intelligence tool that can detect previously invisible disease markers inside single cells. 🧪 https://t.co/wAmNHQ5zhk
@McGillMed @QLSMcGill @NatureComms
mcgill.ca
McGill University researchers have developed an artificial intelligence tool that can detect previously invisible disease markers inside single cells. In a study published in Nature Communications,
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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.
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🚨 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 @ZYM980202 🔗 https://t.co/D7BuB18i3k
#VirtualCell #AI4CureIPF
nature.com
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...
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@KaminskiMed @johnding86 @jonas_schupp @m_konigshoff @GrmyClair @AurelienJustet @Farida_Ahangari @XitingY @ATS_GG @scell_papers @pulmonaryfibros PF Warriors supports innovation in efforts to better understand the lungs and opportunities to improve them, including the responsible use of AI tools.
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1/n Happy to share our new single-cell+unsupervised AI drug discovery method is now published in Nature Biomedical Engineering!🚀 Huge thanks to @johnding86, @KaminskiMed and all collaborators. 🎉 We hope UNAGI can accelerate the discovery of new drugs. https://t.co/StjZE4ebak
nature.com
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...
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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:
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Introducing scSemiProfiler! Our new tool in @NatureComms 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. ( https://t.co/dUpzfhjubz)
nature.com
Nature Communications - Single-cell sequencing is vital for studying complex diseases but is costly. Here, authors introduce scSemiProfiler, a deep generative learning framework that infers...
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"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
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🧬 Introducing scSemiProfiler: Transform large-scale single-cell studies with our cost-effective, accurate tool. Explore semi-profiling with deep generative models & active learning here: https://t.co/mIqdcSm7AV
#SingleCell #Innovation
biorxiv.org
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. Traditi...
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RAMEN identifies effective indicators for severe COVID and Long COVID patients
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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
The ever-increasing availability of single-cell transcriptomic data offers unrivaled opportunities to profile cellular states in various biological processes at high resolution, which has brought...
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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 @McgillMicm . More in the thread 👇 (1/5)
medrxiv.org
Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated...
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New research from @CMUCompBio maps regulatory and signaling networks activated following infection of cells by SARS-Cov-2:
biorxiv.org
Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of...
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Reconstructing SARS-CoV-2 response signaling and regulatory networks
biorxiv.org
Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of...
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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! @KaminskiMed @GMostoslavsky @Awilsonlab
cell.com
Hurley et al. show that a combination of single-cell transcriptomics, computational modeling, and DNA barcoding can map cell-fate trajectories, predicting signaling pathways, transcription factors,...
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Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human…
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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, @AJPLung & to @nih_nhlbi that funded & yes I’m an author https://t.co/8uMn6fyR7l
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Integrating multi-omics longitudinal data to reconstruct networks underlying lung development | American Journal of Physiology-Lung Cellular and Molecular Physiology
journals.physiology.org
A comprehensive understanding of the dynamic regulatory networks that govern postnatal alveolar lung development is still lacking. To construct such a model, we profiled mRNA, microRNA, DNA methyla...
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Cell lineage inference from SNP and scRNA-Seq data
academic.oup.com
Abstract. Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of c
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