We’ve been making phylogenetic trees differentiable :)
Check out our work at #ICML2023 workshops - Sampling and Optimization in Discrete Space (SODS) ፨ and Differentiable Almost Everything (DiffAE) 〆
Looking forward to discuss and learn more! 🙌
❤️ work with Avi & @sokrypton
did a group presentation today with @3blue1brown's manim style transitions. Sharing the template for anyone to build upon
Brought to life with @NarekMaloyan's manim-web + reveal.js + claude + codex
view template: https://t.co/LOSKmrtlXa
view repo: https://t.co/gTb9xxYp7F
@lpachter@3blue1brown@NarekMaloyan wow! just installed, looks pretty cool!
I wonder if manim-web could be another backend for the tool...
The friction I had with manim-slides was, when exporting, things get saved as a video. But I assume manimble can present through python natively so can iterate faster?
really liked ramith aiya’s idea to do this, so wanted to try something fancy as well.
i was reading about world models and decided to make a slide deck for @NVIDIARobotics DreamZero — paper “World Action Model are Zero-shot Policies”
https://t.co/TcyFYYsHlI
This. 100% this.
When I use AI to make slides, my first step is to figure out what I want to say, how I want to say it, and what visuals to use to convey that message.
Only then do I use AI to assemble the slides. I then edit heavily.
Language models can learn new tasks from just a few examples in their prompt, with no retraining. This is called in-context learning (ICL). Now genome language models can do it too.
With a handful of examples, Evo 2 ICL generates proteins, de novo antibodies, CRISPR-Cas9 loci, mitochondrial genomes and entire bacteriophages.
We can even use the synthetic sequences iteratively to construct a chain-of-thought (CoT) and achieve harder design goals.
New work with Yukun A. Hao, @BrianHie and team 🧵
🚀 Excited to share BindClaw! 🦀
Starting from a prompt or research paper, BindClaw connects biological mechanisms to protein design: identify targets and binding sites → build mechanism-specific computational assays → generate and assess binders → refine designs using the results.
We use agents to evaluate whether designs fit the intended biological objective, including competition with a native partner and preference for a particular molecular state. 🧬
In a human-guided calmodulin campaign, 15 of 18 designs showed binding to calcium-loaded CaM, including subnanomolar binders supported by both BLI and SPR.
This work is an incredible team effort together with @DidiKieran , @saint__ai, @ Kyle Gion and @sacdallago and many others!
📄paper/project: https://t.co/MkjU6Swke7
💻code: will be released in a few weeks!
I’d love your feedback on CAPSML: explore virtual Type 1 Diabetes scenarios and compare glucose-control strategies without writing code.
Researchers, educators and anyone curious: give it a try.
For research and education only.
https://t.co/6ZmheHRpIJ
investing time to learn from a cool paper 🔥, @labmlai style by @vpj and team https://t.co/1ibt1D2Cv2.
worth integrating into my dev workflow — using AI to implement/move faster while still investing time to genuinely understand the underlying ideas.
Colchicine is a toxic alkaloid found in some lily species, however this deadly poison can also be used in molecular biology labs or as a medicine for gout
watch on YouTube to support us 👇
https://t.co/Wopsu75yVv