Easy to take for granted, but their peer companies are not doing this type of long term investment.
Money wells are drying up across the trch Industry and ai companies will have to look for funds from adjacent industries like biotech and medicine.
("low hanging fruit", well, not the right way to put it, Google's model are not exactly dumb technology)
> It's not like the model has devised new knowledge. Kind of a low hanging fruit.
Just keep moving goalposts.
The reason you are reading about this is because 1) Gemma has a massive massive PR budget, whereas scientists have zero PR budget, 2) it's coming from Google so it's not the traditional scientists and you know Google and when they publish something new, it's makes it to HN.
I don't see any reason to be excited by the result here. It is a workaday result, using a new tool. I'm usually excited by new tools, but given the source, it's going to take a lot of digging to get past the PR spin, so that extra needless work seems exhausting.
Facebooks's proteins language modelling, followed by Google's AlphaFold, did have really new and interesting methods! But single cell RNA models have been underwhelming because there's no easy "here is the ground truth" out there like there is for proteins. We won't know if this is a significant advancement until years of study show which of the many scRNA foundation models make better predictions. And there was a paper about a year ago that poured a ton of cold water on the whole field: replacing models with random weights barely changed the results on the very limited evaluation sets that we have.
I think Google have only trained what they feel they need, and not a megamodel, but I can't justify this view other as some kind of general feeling. They obviously know enough to make excellent models though, so I doubt they're behind in any meaningful sense.
Then you use a combination of the best models to amplify your training set, and enhance it for the next iteration. And then repeat the process at gen n+1.
But what I’ll say is, ideally they would demonstrate whether this model can perform any better than simple linear models for predicting gene expression interactions.
We’ve seen that some of the single cell “foundation” models aren’t actually the best at in silico perturbation modeling. Simple linear models can outperform them.
So this article makes me wonder: if we take this dataset they’ve acquired, and run very standard single cell RNA seq analyses (including pathway analyses), would this published association pop out?
My guess is that yes… it would. You’d just need the right scientist, right computational biologist, and right question.
However, I don’t say this to discredit the work in TFA. We are still in the early days of scSeq foundation models, and I am excited about their potential.
However to infer or predict celular acitivities you need a ton of domain knowledge and experties about particular cell types, biological processes and specific environments. Typically the successful ones are human curated and validated (e.g large interaction networks based on literature).
In cancer it's even more unpredictable because of the lack of good (experimental) models, in-vivo or in-vitro, representing what actually happens the clinically and biologically underneath. Given the single cell resolution, its uncertainty will also amplify because of how heterogeneous inter- and intra- tumours are.
Having said that, a foundation model is definitely the future for futher development. But with all of these things, the bigger the model, the harder the validation process.
- EPS3.9: Polysaccharide (deep sea bacterium sugar, fermentable, induces IFN-1) causes Pyroptosis causes IFN-1 causes Epitope Spreading (which is an amplifying effect) causes anti-cancer response.
- CPMV; Cow-Pea Mosaic Virus (is a plant virus that doesn't infect humans but causes an (IFN-1 (IFN-alpha and a lot of IFN-beta)) anti-cancer response in humans. Cow Pea consumption probably used to be even more prevalent in humans before modern agriculture; cow peas may have been treating cancer in humans for thousands of years at least.)
I emailed these potential new treatments to various researchers with a fair disclaimer; but IDK whether anything has been invested in developing a treatment derived from or informed by knowledge of the relevant pathways affected by EPS3.9 or CPMV.
There are RNA and mRNA cancer vaccines in development.
Without a capsid, RNA is destroyed before arrival. So RNA vaccines are usually administered intramuscularly.
AFAIU, as a general bioengineering platform, CPMV Cow-Pea Mosaic Virus could also be used like a capsid to package for example an RNA cancer vaccine.
AFAIU, CSC3.9 (which produces the "potent anti-cancer" EPS3.9 marine spongiibacter polysaccharide) requires deep sea pressure; but it's probably possible to bioengineer an alternative to CSC3.9 which produces EPS3.9 in conditions closer to ambient temp and pressure?
> Would there be advantages to (CPMV + EPS3.9) + (CPMVprime + mRNA)? (for cancer treatment)
I get your sentiment of "why you gotta bring down this good thing" but the answer to your actual question is battle scars from the constant barrage of hostile lies and whitewashing we are subject to. It's kind of absurd (and mildly irresponsible) to think "THIS time will be the time things only go well and nobody uses the new thing for something I don't want".
https://en.wikipedia.org/wiki/COVID-19_misinformation#Virus_...
I do not endorse the view that covid was engineered. Also, I consider it to be unrelated to what I am concerned about, and I will kindly explain it to you:
Traditional labs work with the wet stuff. And there are a lot of safeguards (the levels you mentioned didn't came out of thin air). Of course I am in favor of enforcing the existing safeguards to the most ethical levels possible.
However, when I say that I am concerned about AI being used to circumvent international agreements, I am talking about loopholes that could allow progress in the development of bioweapons without the use of wet labs. For example, by carefully weaving around international rules and doing the development using simulations, which can bypass outdated assumptions that didn't foresaw that this could be possible when they were conceived.
This is not new. For example, many people were concerned about research on fusion energy related to compressing fuel pellets, which could be seen as a way of weaving around international treatises on the development of precursor components to more powerful nuclear weapons (better triggers, smaller warheads, all kinds of nasty things).
