Starting to get a little stir crazy so it’s time for the unpopular opinions game. Gimme a topic, I’ll give you an opinion.
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Replying to @s_r_constantin
one legitimately exciting use for maching learning, and one overhyped stupid use please
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Replying to @gravity_levity
exciting: vaccine optimization. overhyped: QSAR.
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Replying to @s_r_constantin @gravity_levity
what's a good resource on the former?
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Replying to @jsdenain @gravity_levity
what little I know I learned from Alex Rubinsteyn http://www.rubinsteyn.com/ and keeping up with new biotech startup news
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also rough intuition: the number and distribution of cell surface proteins & antibodies is in the range where ML seems like a big improvement (huge diversity between individuals, combinatorial explosion making traditional stats impractical)
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and you can have a tight in-silico/in-vitro feedback loop for designing/optimizing this stuff
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this seems plausible for, say, antibody design, but less so for vaccines how are you envisioning this would work?
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I was thinking like cancer vaccines where you have a tumor cell culture? could be wrong though
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but there's no immune system for a vaccine to work with there
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https://www.jimmunol.org/content/jimmunol/153/12/5586.full.pdf?casa_token=MDdmL42octEAAAAA:I_nVsU-O3_LLnWXB-ENO_1vWZyYR0A3aYG4bPLm8BekORgWAejr7gKw47kWepjnNUvB1bdC76YQdff4N … apparently MHC binding affinity is a good proxy for immunogenicity
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yeah that could be useful would be interesting to explore anyway
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Replying to @halvorz @s_r_constantin and
I think this is underway already without necessarily using machine learning, e.g. sequencing tumor cells, using an algorithm to predict epitopes likely to be presented by MHC I, trying to engineer T cells to be specific for those epitopes.
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