Tweetovi
- Tweetovi, trenutna stranica.
- Tweetovi i odgovori
- Medijski sadržaj
Blokirali ste korisnika/cu @MoAlQuraishi
Jeste li sigurni da želite vidjeti te tweetove? Time nećete deblokirati korisnika/cu @MoAlQuraishi
-
Prikvačeni tweet
I’m late to my own party but excited to share our new work on predicting SLiM-mediated protein-protein interactions, out today in
@naturemethods with Joe Cunningham,@GregKoytiger, and@sorger_peter! A blogpost is forthcoming but for now a tweetstorm (1/8)https://www.nature.com/articles/s41592-019-0687-1 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Excited to see this out ! Wonderful design by
@pgainza and great collaboration with our@unil@OncoUNILCHUV@Ludwig_Cancer colleagues! Looking forward for the next iterations!https://twitter.com/NatureBiotech/status/1224388616367022081 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
I’m thrilled to announce the publication of the quantitive proteomic profiling of the Cancer Cell Line Encyclopedia. The
@GygiLab in collaboration with colleagues at Novartis and the Broad we measured the proteomes of 375 cell lines from various tumor lineages (1/13)pic.twitter.com/WXgsODMzFs
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Had an early look at this work and it’s really impressive stuff! Demonstrates the remarkable power of semi-supervised learning in very low N contexts.https://twitter.com/grigonomics/status/1220768850834284544 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Here is the promised blogpost: https://protocolsmethods.springernature.com/users/346093-mohammed-alquraishi/posts/58576-imbuing-machine-learning-with-a-hint-of-biophysics … 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.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
One of the hardest things to figure out as a PI, especially a young PI, is "when to hold em and when to fold em" with respect to appealing/revising a paper with tough reviews. A few thoughts
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Happy to share the results of a fun project with
@lexy_von_diez:#caulobactercresentus poles are@BioCondensates reinforcing asymmetry through selective sequestration of signaling proteins. Wonderful collaboration between@MoernerLab and Lucy Shapiro lab.https://www.nature.com/articles/s41564-019-0647-7 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Interested in doing a PhD with me
@uni_copenhagen on#machinelearning for improving our understanding of#proteins? Open PhD call https://di.ku.dk/ominstituttet/ledige_stillinger/talent-25-phd-positions/ …. Get in touch if you would like to apply (and meet the eligibility requirements). Deadline Feb 13.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Glad to see
@DeepMindAI’s AlphaFold paper finally out. I had the pleasure of being one of the reviewers and getting to write the accompanying@NatureNV article. The future of protein structure prediction is looking very bright!https://twitter.com/mvicaracal/status/1217553842272907266 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Glycans finally join proteins, DNA, and RNA in getting the language model treatment. Great work by
@daniel_bojar@DiogoMCamacho and Jim Collins. Even better, their model is named SweetTalk. https://www.biorxiv.org/content/10.1101/2020.01.10.902114v1 …pic.twitter.com/BkNF0mTByK
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
If I understood his point, it is not about the difficulty of the questions. It is not about being hard to know how the brain works for example, but the difficulty in the concepts/theory. Are there theories that explain biology that are hard to understand?
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Does biology have truly difficult ideas? Studying math, comp sci, physics, etc., one quickly encounters material that is hard to truly grasp (and many of us get to a point where we mentally just can't go further, while some others can). Does this exist in biology? If not, why?pic.twitter.com/XFGnSfP2df
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Is there a list of junior & mid-career PIs that do machine learning for computational biology somewhere on twitter? Would be very useful to consult for speaker invitations, PCs, reviewers ...
@michaelhoffman ?Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Dream Kinase prediction challenge paper is now on Biorxiv! (Unofficial) top performing model by yours truly
@DR_E_A_Mhttps://www.biorxiv.org/content/10.1101/2019.12.31.891812v2.supplementary-material …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
Protein sequence design with a learned potential https://biorxiv.org/cgi/content/short/2020.01.06.895466v1 …
#biorxiv_bioinfoHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mohammed AlQuraishi proslijedio/la je Tweet
At this rate, Cryo-EM will have overtaken X-ray as the most common technique for new PDB structures in ~4 years.https://twitter.com/mkikkawa/status/1214335529627475969 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Finally what makes this particularly special for me is that it’s Joe’s first paper! Joe came to work with me and Peter Sorger as an MIT undergraduate and it has been a singular joy to work with him throughout this time. His only vice is that he refuses to get on Twitter ;-) (8/8)
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The code is on GitHub (https://github.com/aqlaboratory/hsm …), and there’s a website for interactive predictions and visualizations as well as bulk downloads (http://proteinpeptide.io ) (7/8)
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
For now we used it to do a reasonably standard network-level analysis of the human proteome, which has revealed interesting aspects of the topological organization of peptide-binding domains. Stay tuned on this front however as we’ll have a lot more to say very soon (6/8)pic.twitter.com/2bqjykWrzW
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
It’s also mechanistic in the sense of providing residue-level energetic and structural insights consistent with our understanding of the domain families we have modeled (SH2s, SH3s, PDZs, WWs, PTBs, WH1s, and tyrosine kinases and phosphatases) (5/8)pic.twitter.com/nwu99VyRaW
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The model is mechanistic and interpretable in the sense that we can decompose a multidentate protein-protein interaction, comprised of multiple peptide-binding domains and SLiMs, in terms of the underlying interactions between them (4/8)pic.twitter.com/O7e8y2bMDQ
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.
- a framework for low-N protein engineering with data-efficient deep learning! Had a blast working with brilliant