A little late to the Twitter party, but happy to share my first (co-)first author publication is finally out in @Nature! A dream collab with @vulcnethologist@GillisDub exploring the role of #dopamine in real-time action selection in naive mice. More below from @Datta_Labππ½π₯
deleted this app six months ago and became suspiciously well-adjusted. spent the time building something cool. but i missed strangers' opinions too much.
hello timeline goblins, i'm back to ruin all that progress with you
(will explain the thing eventually, goblin's honor)
@owl_posting I wonder if in this model the training of a pharmacist needs to morph closer to that of a doctor in order to maintain guardrails while still enabling self-experimentation/treatment.
or you enable some interesting algorithmic personalized pricing to disincentivize reckless use
@cgeorgiaw@AAlphaBio Curious what you think about the merits of yeast assays (like Y2H) vs AP-MS assays and their value and fidelity for measuring PPIs at scale. Useful data, but is it the best way to measure?
@parmita Every paper that claims that some multi-layer gizmo like this is going to magically give rise to some "emergent virtual cell" causes my eyes to roll so far back into my head that i can see my amygdala on fire
Awesome list @chrisbarber! @LilaSciences is hiring across a ton of areas including RL, infra, ML Ops, ML for bio and materials. If youβre at NeurIPS please reach out or come join us at our happy hour!
RSVP: https://t.co/xuRcQwuhfG
A big chunk of the team will be there and will be a great opportunity to learn more about Lila
President Trump is launching the most powerful scientific platform to ever be built, reminiscent of the Manhattan Project and Apollo programs: Genesis Mission.
@iskander If you can get access, Causaly is highly specialized for biomedical literature and drug targets. Seconding @elicitorg and maybe @perplexity_ai for completeness.
@zavaindar@incredutility Even if 0 shot binder design approaches solved, how much of a bottleneck is screening for X-reactivity/specificity?
Not to mention all the other developability/PK-in-human challenges, which are more well accepted open grand challenges
Transcriptomics is very useful but overdiscussed because measurement tools are becoming mature. No one is actually ready for the complexity of proteomics. Can be thousands of proteoforms for a single protein, all with identical mass, near identical fragmentation patterns, very low abundance. Crazy numbers here: 10^6-10^8 *types* per cell and ~10^10 total counts. Most current tools (eg. many MS flavors) work with bulk samples and even then cannot pick out subtle chemical differences, blending distinct molecules and obfuscating potentially important biochemistry.