The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Johnny Yu no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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Answered raw tape D 4 · C 4 · P 4 · Cm 4 4.00

Q I'm not asking this question the correct way, but you will understand the gist of it. How do you decide where in the genetic landscape to start? How do you choose perturbations?

A I think you want to match, and this goes kind of the same with drugs, is you want to match your quest, your, like, perturbation toolkit, which is like the kind of arrows you throw at the biology against the biology you have. So for cancer, that means going after cancer-relevant genes, genes that impact growth of cells, um, genes that impact DNA regulation, and also drugs that target key cancer pathways. So I think for cancer-relevant questions, but This data set, even though it's heavily based around these kinds of chemical perturbations of cancer, they also, these pathways are so conserved and fundamental that they broadly apply to the neuroscience space or like to just immune cell development in general. So I think it's really the foundation model that's going to be able to take this data, ingest it, build a model, and then train, and then understand basically how to like translate that data to a different context entirely. Yeah, so this is the key. I think this is one of the really special things we have at Vivo, and it's this mosaic platform. So it allows us to take cells from many different patients, and then in cancer, this means all kinds of cancer, lung cancer, non-pancreatic cancer, et cetera, et cetera, from different patients which have their own special genetics, and pool them together into a single mosaic tumor, which we then can reproducibly screen hundreds or tho…

AI assessment note: “you want to match... your perturbation toolkit... against the biology you have”

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