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Greg Koytiger
VP, Head of AI Products @
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Mohammed AlQuraishi 23. sij
Here is the promised blogpost: on the motivation behind our modeling / ML approach for protein-peptide interactions. We'll likely have another post soon focused more on the (structural) biology.
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Mohammed AlQuraishi 15. sij
Glad to see ’s AlphaFold paper finally out. I had the pleasure of being one of the reviewers and getting to write the accompanying article. The future of protein structure prediction is looking very bright!
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Greg Koytiger 14. sij
Odgovor korisniku/ci @gaurav_bio @vboykis
I gonna fit a Transformer to that and get SOTA
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emedgene 10. sij
Researchers developed a bespoke machine-learning approach, hierarchical statistical mechanical modelling, for the accurate prediction of protein–peptide interactions across multiple protein families.
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Greg Koytiger 8. sij
Odgovor korisniku/ci @GregKoytiger
For me, the most important take away is Figure 5 - that current machine learning approaches are of higher fidelity than high throughput experiments! For another example, see Figure 2b from the Nature Methods paper yesterday
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Greg Koytiger 8. sij
Dream Kinase prediction challenge paper is now on Biorxiv! (Unofficial) top performing model by yours truly
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François Chollet 7. sij
Our field isn't quite "artificial intelligence" -- it's "cognitive automation": the encoding and operationalization of human-generated abstractions / behaviors / skills. The "intelligence" label is a category error
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Mohammed AlQuraishi 7. sij
I’m late to my own party but excited to share our new work on predicting SLiM-mediated protein-protein interactions, out today in with Joe Cunningham, , and ! A blogpost is forthcoming but for now a tweetstorm (1/8)
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Kevin Yang 楊凱筌 7. sij
Joseph Cunningham Peter Sorger and use energy-based ML to predict protein-peptide interactions. Their model is interpretable, naturally incorporates physical "priors", and outperforms high-throughput experiments!
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Tim Urban 6. sij
The path of a maturing thinker. In order to get to Grown-Up Mountain and start real learning, you have to brave the cold winds of Insecure Canyon. If you're not willing to say "I don't know" for a while, you might spend your whole life on Child’s Hill.
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Mohammed AlQuraishi 31. pro
A piece of holiday-time reflection: one thing I’m grateful about in science is the existence of a real field-wide community, made more visible by Twitter. I suspect this is less true in other professions and is a genuinely positive feature.
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Lara Yuan 25. pro
Happy holidays from the Cascade team (minus a few)! It's been an amazing year with these incredible people, here's to an even better 2020!
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Arvind Narayanan 24. pro
Reminder: student evaluations of teaching tend to show stark racial and gender biases, and don't actually measure teaching effectiveness. A statement endorsed by 22 scholarly associations calls for limiting the role of student ratings in faculty review:
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XKCD Comic 27. stu
How To Deliver Christmas Presents
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Kevin Yang 楊凱筌 25. stu
Cool paper on combining meta- and active learning to efficiently learn protein function from protein sequence, from Rainier Barrett and
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Mohammed AlQuraishi 19. stu
Great work using inter-residue orientations to exceed AlphaFold’s performance on protein structure prediction by Jianyi Yang, Ivan Anishchenko, and others from the Baker lab: . First heard about this at RosettaCon and I’m very glad to finally see it out!
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Vala Afshar 13. stu
This is how octopuses use camouflage in the wild
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cj battey 4. stu
And the oscar for best figure goes to ...
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Daniel MacArthur 31. lis
Your annual reminder of arguably the greatest thread of science Twitter poetry of all time. To all scientists this Halloween eve: may the sparrow of doubt hover close enough to guide you to rigor, but not so close that you are paralyzed into inaction. 🎃
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Tami Lieberman 29. lis
Engineering orthogonal signalling pathways reveals the sparse occupancy of sequence space. Congrats on the great paper
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