Amin Emad
@AminMEmad
Followers
26
Following
1
Media
0
Statuses
8
Associate Professor at McGill University, Director of Computational Biology and Artificial Intelligence (COMBINE) lab
Montreal
Joined November 2017
A wonderful collaboration between my lab @COMBINE_McGill , H. Goodarzi @genophoria, H. Najafabadi @hsnajafabadi and V. Ramani @vram142. Read more about it in the thread below:
We are excited to introduce LoRNA-SH, a frontier RNA foundation model that leverages long-read transcriptomics and long-context inputs to learn and predict the transcriptome architecture with base-pair resolution:
0
1
3
Our GRN-guided scRNA-seq simulator, GRouNdGAN, is published in @NatureComms ! Discover how it works and bridges the gap between synthetic and real-data benchmarks of GRN inference methods. Read the thread 🧵👇 and the paper: https://t.co/QpynCYTaZe.
nature.com
Nature Communications - Benchmarking GRN inference methods remains a challenge. Here, authors present GRouNdGAN, a causal generative model that imposes a user-defined GRN in its architecture to...
1
2
2
.@COMBINE_McGill @AminMEmad present GRouNdGAN, a causal generative model that imposes a user-defined GRN in its architecture to simulate realistic single-cell data. #GRNInference @NatureComms #BiotechNatureComms
https://t.co/5ZfCb7LSB3
nature.com
Nature Communications - Benchmarking GRN inference methods remains a challenge. Here, authors present GRouNdGAN, a causal generative model that imposes a user-defined GRN in its architecture to...
0
3
5
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 @AminMEmad, Simon Rousseau, and @KTremblay21. Read more in this 🧵 about our pre-print: https://t.co/k2VU1CUjTa
medrxiv.org
Defining the molecular mechanisms of novel diseases such as COVID-19 is crucial to identify treatable traits to improve patient care. To circumvent a priori bias and the lack of in-depth knowledge of...
2
2
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 @Nature_NPJ!
1
3
1
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 https://t.co/nlpP08JFvX
1
2
0
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_Qc for their support
0
3
4
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...
1
5
2