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…

We're very glad to be working alongside Dr. Park towards #AI driven in-silico clinical trials for drug combinations, and are thankful to @GenomeQuebec Oncopole & IVADO for their support bit.ly/3692wvV

To find potential drug targets for #COVID19 we used probabilistic graphical models to find regulation & signaling changes in cells infected w/ SARS-CoV-2 & build 'COVID-19-relevant' transcriptional regulatory networks. Details in 🧵👇 and doi.org/10.1101/2020.1…

Transcriptional regulatory networks can tell us what is happening in diseased cells. Most methods don’t use info about phenotypic/clinical labels of samples in building these nets using expression data. A 🧵about our new method, InPheRNo in npj Journals! is.gd/SAyEnX

3yrs into the #COVID19 #pandemic it is pressing that we learn to treat it & understand its molecular mechanism. We’re excited to share a collab between the labs of Amin Emad, Simon Rousseau, and @KTremblay21. Read more in this 🧵 about our pre-print: medrxiv.org/content/10.110…

.Emad's COMBINE Lab @ McGill University Amin Emad present GRouNdGAN, a causal generative model that imposes a user-defined GRN in its architecture to simulate realistic single-cell data. #GRNInference Nature Communications #BiotechNatureComms nature.com/articles/s4146…

Our GRN-guided scRNA-seq simulator, GRouNdGAN, is published in Nature Communications ! Discover how it works and bridges the gap between synthetic and real-data benchmarks of GRN inference methods. Read the thread 🧵👇 and the paper: doi.org/10.1038/s41467….

A wonderful collaboration between my lab Emad's COMBINE Lab @ McGill University , H. Goodarzi Hani Goodarzi, H. Najafabadi Hamed Najafabadi and V. Ramani Vishnu Ram. Read more about it in the thread below: