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Wet-lab innovations will lead the AI revolution in biology

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Re: Wet-lab innovations will lead the AI revolution in biology

#11

Post by wombatpm »

plaidfuji wrote: post #5

> Get those linguists out of here, more data will replace whatever insights they have! It’s a fun and increasingly popular stance to take. And, to a degree, I agree with it. More data will replace domain experts, the bitter lesson is as true in biology as it is in every other field. I think it’s fundamentally shifting how people approach R&D in all physical fields. The power of “the ML way” is almost a self-fulfillin…

In grad school (I was in Chemical engineering ) I took molecular biology course. We read/reviewd a number of papers in different areas. For my review I proposed a series of experiments to answer questions raised by the paper. It was very logical and well thought out. Problem was it would have amounted to 3grad students full time for at least a year. Once you see the effort involved you can see why the ML approach is exciting

Re: Wet-lab innovations will lead the AI revolution in biology

#13

Post by throwawaymaths »

jdeaton wrote: post #2

> people unacquainted with biology have a false perception of how low-throughput biology experimentation is. In many ways, it can be. But the underlying physics of microbiology lends itself very well to experiments that could allow one to collect tens-of-thousands, if not millions, of measurements in a singular experiment. It just needs to be cleverly set up. I think this passage gets to the fundamental rift of disag…

The old joke by Sydney Brenner: "low input, high throughput, no output" science.

Re: Wet-lab innovations will lead the AI revolution in biology

#14

Post by throwawaymaths »

wombatpm wrote: post #11
plaidfuji wrote: post #5

> Get those linguists out of here, more data will replace whatever insights they have! It’s a fun and increasingly popular stance to take. And, to a degree, I agree with it. More data will replace domain experts, the bitter lesson is as true in biology as it is in every other field. I think it’s fundamentally shifting how people approach R&D in all physical fields. The power of “the ML way” is almost a self-fulfillin…

In grad school (I was in Chemical engineering ) I took molecular biology course. We read/reviewd a number of papers in different areas. For my review I proposed a series of experiments to answer questions raised by the paper. It was very logical and well thought out. Problem was it would have amounted to 3grad students full time for at least a year. Once you see the effort involved you can see why the ML approach is…

How exactly would ML speed it up something that takes 3 grad students full time?

Listen. A lot of this shit gets discovered for crazy reasons. For two years these two postdocs were throwing away one fraction of their size exclusion chromatography step. I got into a really heated six hour argument where i insisted that the postdocs did not understand that size exclusion chromatography, big shit comes out first (they thought that big shit comes out last). The next day the postdoc apologized, since I was correct.

Oddly, a month or so later, they stopped to take a look at the fraction they were throwing out and it turned out that their molecule was self-assembling into cages. This is important for how the molecule is supposed to work. They got some very important papers out of it. I'm not even thanked.

ML is not accelerate this sort of stuff.

Re: Wet-lab innovations will lead the AI revolution in biology

#15

Post by dexwiz »

bartread wrote: post #3
jdeaton wrote: post #2

> people unacquainted with biology have a false perception of how low-throughput biology experimentation is. In many ways, it can be. But the underlying physics of microbiology lends itself very well to experiments that could allow one to collect tens-of-thousands, if not millions, of measurements in a singular experiment. It just needs to be cleverly set up. I think this passage gets to the fundamental rift of disag…

You have just very eloquently expressed why I left a career in biochemistry behind after undergrad. Realistically I had no business doing that degree in the first place: I simply don't have the patience for the lab work grind.

Same reason I left Chemistry. Gotta grind for 80 hours a week for a decade for a chance at a good position. And hope you don’t get stuck under a PI that steals all the credit.

Re: Wet-lab innovations will lead the AI revolution in biology

#16

Post by staunton »

throwawaymaths wrote: post #14
wombatpm wrote: post #11

Earlier quoted context omitted.

In grad school (I was in Chemical engineering ) I took molecular biology course. We read/reviewd a number of papers in different areas. For my review I proposed a series of experiments to answer questions raised by the paper. It was very logical and well thought out. Problem was it would have amounted to 3grad students full time for at least a year. Once you see the effort involved you can see why the ML approach is…

How exactly would ML speed it up something that takes 3 grad students full time? Listen. A lot of this shit gets discovered for crazy reasons. For two years these two postdocs were throwing away one fraction of their size exclusion chromatography step. I got into a really heated six hour argument where i insisted that the postdocs did not understand that size exclusion chromatography, big shit comes out first (they t…

A hypothetical good AI would have reviewed the experimental design and pointed out the misconception

Re: Wet-lab innovations will lead the AI revolution in biology

#18

Post by hirenj »

I read a great piece from Michael Bronstein about this very topic earlier this year.

https://towardsdatascience.com/the-road-to-biology-2-0-will-...

I think an important point raised here is the distinction between good data, and the "relative" data present in a lot of biology. As examples from the article, a protein structure, or genome/protein sequence data is good data, but data like RNA-seq or mass spectrometry data is relative (and subject to sensitivity / noise etc). The way I like to think of it is that sequence data and structural data is looking at the actual thing, but the relative data only gets you a sliver of a snapshot of a process. Therefore it's easier to build models to capture relationships between representations of real things, rather than models where you can't really distinguish between signal and noise. I spend a fair amount of time these days trying to figure out how to take advantage of good data to gain insights into things where we have relative data.

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