John Le Quesne retweeted
A lot of molecular and cell and developmental biology is concerned with figuring out the "functions" of various proteins. I'm starting to distrust that formulation...
It was fantastic having the opportunity to speak at Worldcon 2024 about cancer, and its role as a metaphor in science fiction yesterday. Such a good experiemce - great audience and brilliant questions and discussions. @UofGlasgow @CRUK_SI #WorldCon
John Le Quesne retweeted
@Molly_McKenzie2 has just published her first first-author publication! So much to know about NFkB signaling in this review!
Enjoy! 🎉🎊
Really excited to share my first review article! Have a look if you are interested in NFkB signalling in colorectal cancer! 👩🔬
NFκB signalling in colorectal cancer: examining the central dogma of I... sciencedirect.com/science/ar…
Okay so we’re finally getting a handle on how biological plasticity underpins and may cause some of the most deadly morphologies in lung adenocarcinoma, thanks to Hannah Williams’ awesome preprint:
biorxiv.org/content/10.1101/…
Only possible through amazing bioinf from Nicolas Poulain, next-level deep ohenotyping from Leah Officer-Jones and team (@AkoyaBio tech), and superb whole-transcriptome @nanostringtech GeoMx data from the @nigeljamieson group.
FFPE rules! @UofGlasgow @CRUK_SI @unibern
The last few days have been superb. It's been a real privilege to co-lead the TRANSLACORE biobanking training school with Neşe Atabey. The Izmir Biomedicine and Genome Centre have been amazing hosts.
Three talks from the world of Scottish tissue research!
John Le Quesne retweeted
Great to see @LeQuesneLab recognised for his teams work on using AI to ‘learn the language of cancer’ and provide faster diagnosis. independent.co.uk/news/healt…
John Le Quesne retweeted
Curious about AI's potential in Pathology? Discover how self-supervised AI autonomously learns histologic patterns: no need for experts to perform tedious annotations! Dive into our latest research: rdcu.be/dKtEp #PrecisionPathology @NYUGSOM_PMED @NYUGSOM_Path
John Le Quesne retweeted
So glad to see HPL out. An incredible collaboration with @artsinyc and @LeQuesneLab. Amazing work from @Adalberto_Cq, Nicolas Coudray and others at @GlasgowCS, @UofGCancerSci, @CRUKSI, @NYUGSOM_PMED, @NYUGSOM_Path. #AI #Pathology
Histomorphological Phenotype Learning, a self-supervised deep learning method to automatically discover clinically-relevant features in cancer histology images @NatResCancer nature.com/articles/s41467-0…
John Le Quesne retweeted
A fantastic collaboration with @Adalberto_Cq, Nicolas Coudray, @LeQuesneLab, @keyuan1, and more! @glasgowunisrc @nyugrossman
John Le Quesne retweeted
A computer system harnessing #AI to decipher the language of #cancer accurately detects disease signs and offers reliable patient outcome predictions says researchers of Glasgow and New York University. #MIG @UofGlasgow @nyuniversity @keyuan1 @LeQuesneLab
mediaindia.eu/society/scient…
John Le Quesne retweeted
A new AI-powered system developed by researchers at @GlasgowCS, @UofGCancerSci and @nyulangone can spot signs of cancer in samples with remarkable accuracy.
It could help human pathologists and improve cancer care in the future.
Read more: gla.ac/4bRl98v
ALT Dr Adalberto Claudio Quiros, Dr Ke Yuan and Professor John Le Quesne stand in a data centre at the University of Glasgow’s School of Computing Science.
I’m so very proud of this work, with my CS colleagues in Glasgow and NYU. The pathology implications are HUGE. Self-learning AI for fully interpretable democratised quantitative H&E image analysis and accurate prognostication. gla.ac.uk/news/headline_1079…
nature.com/articles/s41467-0…
Adal, Nicolas and the team have really changed the game with this study. Interpretable quantitative and immediately useful AI-pathology is here. The implications for both diagnostics and research are huge. #UoG #NYU
Crucial conclusions from MARS-2. Radical mesothelioma surgery does not extend life. #WCLC
John Le Quesne retweeted
What AI can teach us about the nitty-gritty of patterns in pathology slides? We show that self-supervised learning can identify patterns that predict survival, have distinct molecular profiles, and most interestingly help pathologists find previously unappreciated clues.
🔬Our latest-preprint: self-supervised AI automating histologic pattern discovery and enhancing collaboration with pathologists. Check it out: arxiv.org/abs/2205.01931 with @Adalberto_Cq Nicolas Coudray @LeQuesneLab @keyuan1 et al.! #PrecisionPathology @NYUGSOM_Path @NYUGSOM_PMED
John Le Quesne retweeted
🔬Our latest-preprint: self-supervised AI automating histologic pattern discovery and enhancing collaboration with pathologists. Check it out: arxiv.org/abs/2205.01931 with @Adalberto_Cq Nicolas Coudray @LeQuesneLab @keyuan1 et al.! #PrecisionPathology @NYUGSOM_Path @NYUGSOM_PMED
