Kaiyi Jiang
@idmjky
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PhD at MIT @MITdeptofBE in AbuGoot and Birnbaum lab. Previously undergraduate with Caleb Bashor @BashorLab and Gang Bao @Rice_BIOE
Joined March 2014
Thrilled to share that EVOLVEpro is now published @ScienceMagazine Since our preprint, we now demonstrate low-N eningeering of both antibody and enzymes. We hope this model will broadly useful to the protein engineering field.
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@Ella_Maru @ScienceMagazine We haven’t tested this but I do think the concept is transferable. There might be other design rules you have to deal with depending on what models you use.
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This project was my introduction to syn bio in undergrad with @XiaoyuYang6 @BashorLab and you really can't imagine a better project than this to learn rational protein engineering and signaling network.
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RT @BrianHie: In new work led by @aditimerch with @samuelhking, we prompt engineer Evo to perform function-guided protein design with high…
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RT @JerzySzablowski: qPCR can measure expression of virtually any gene... in homogenized tissues. Here, we show an "in-vivo qPCR" equivalen…
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RT @alexrives: Introducing ESM Cambrian. Unsupervised learning can invert biology at scale to reveal the hidden structure of the natural w…
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RT @omarabudayyeh: EVOLVEpro, our general AI model for protein engineering, is now out in Science! We show the ability to design and evolv…
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@qbilius @ScienceMagazine We haven’t in our work. We mostly stayed around 20/r. However, I do think it would benefit from more data per round if you look at figure 1 pertaining with increasing amount of data does show a significant increase in performance!
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