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AlphaGenome: AI for better understanding the genome

deepmind.google

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Re: AlphaGenome: AI for better understanding the genome

#181

Post by mensetmanusman »

MattRix wrote: post #167
diggan wrote: post #161

Earlier quoted context omitted.

All of what you said makes sense from the perspective of a product manager working for a for-profit company trying to maximize profit either today or eventually. But the submission blog post writes: > To advance scientific research, we’re making AlphaGenome available in preview via our AlphaGenome API for non-commercial research, and planning to release the model in the future. We believe AlphaGenome can be a valuabl…

I feel like this take is missing a sense of balance. You can have a goal of advancing scientific research while also still making money. You don’t have to choose one extreme end of the scale. I’d argue that the product providing some monetary value for Google will help ensure that this team doesn’t get moved some more profitable project instead. That way they can continue improving this tool and make more tools like…

Working in research and development for 20 years, I can assure you that the only science that leaves the lab is that which can make money.

Re: AlphaGenome: AI for better understanding the genome

#182

Post by [deleted] »

LarsDu88 wrote: post #110

You know the corporate screws are coming down hard, when the model (which can be run off a single A100) doesn't get a code release or a weight release, but instead sits behind an API, and the authors say fuck it and copy-paste the entirety of the model code in pseudocode on page 31 of the white paper. Please Google/Demis/Sergei, just release the darn weights. This thing ain't gonna be curing cancer sitting behind an…

[deleted]

Re: AlphaGenome: AI for better understanding the genome

#183

Post by pfisherman »

diggan wrote: post #161
wrsh07 wrote: post #123

Earlier quoted context omitted.

This is a strange take because this is consistent with what Google has been doing for a decade with AI. AlphaGo never had the weights released. Nor has any successor (not muzero, the StarCraft one, the protein folding alphafold, nor any other that could reasonably be claimed to be in the series afaik) You can state as a philosophical ideal that you prefer open source or open weights, but that's not something deepmind…

All of what you said makes sense from the perspective of a product manager working for a for-profit company trying to maximize profit either today or eventually. But the submission blog post writes: > To advance scientific research, we’re making AlphaGenome available in preview via our AlphaGenome API for non-commercial research, and planning to release the model in the future. We believe AlphaGenome can be a valuabl…

Key question is the license they attach to model and weights. I have been seeing an increasing amount of releases in this space under non-commercial licenses.

I think companies in the space should either totally open source or not publish at all.

I can see publishing like this as achieving one (or more) of a several objectives:

1. Marketing software to for sales / licensing

2. Marketing startup to investors

3. Crowdsourcing use cases or product features from academia

Now here are the problems with those:

1. Selling software (exclusively) to drug companies is a terrible business model. Very low ceiling there. You can make more from one drug.

2. Indicates company focus is producing models and not drugs. See point one.

3. Computational labs want to release open source, so not viable to build on restricted tooling. Experimental labs may just be using to algo-wash prior hypotheses / biases.

Now weigh against disadvantage of letting competitors know what you are working on, how far you have progressed, as well as your methods.

Re: AlphaGenome: AI for better understanding the genome

#184

Post by dekhn »

Kalanos wrote: post #164

The functional predictions related to "non-coding" variants are big here. Non-coding regions, referred to as the dark genome, produce regulatory non-coding RNA's that determine the level of gene expression in a given cell type. There are more regulatory RNA's than there are genes. Something like 75% of expression by volume is ncRNA.

There is a big long-running argument about what "functional" means in "non-coding" parts of the genome. The deeper I pushed into learning about the debate the less confident I became of my own understanding of genomics and evolution. See https://www.sciencedirect.com/science/article/pii/S096098221... for one perspective.

Re: AlphaGenome: AI for better understanding the genome

#186

Post by cysteinechapel »

bglazer wrote: post #117
RivieraKid wrote: post #74

I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.

Molecular dynamics describes very short, very small dynamics, like on the scale of nanoseconds and angstroms (.1nm) What you’re describing is more like whole cell simulation. Whole cells are thousands of times larger than a protein and cellular processes can take days to finish. Cells contain millions of individual proteins. So that means that we just can’t simulate all the individual proteins, it’s way too costly an…

Scales can also decouple from each other. Complex trait genetic variation at the whole genome level acts predominantly in an additive fashion even though individual genes and variants have clearly non-linear epistatic interactions.

Re: AlphaGenome: AI for better understanding the genome

#187

Post by cysteinechapel »

mbeavitt wrote: post #158
RivieraKid wrote: post #74

I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.

Simulating the real world at increasingly accurate scales is not that useful, because in biology - more than any other field - our assumptions are incorrect/flawed most of the time. The most useful thing simulations allow us to do is directly test those assumptions and in these cases, the simpler the model the better. Jeremy Gunawardena wrote a great piece on this: https://bmcbiol.biomedcentral.com/articles/10.1186/1…

Plenty of simple models in biology that don't model the underlying details provide profoundly generalizable insights across scales. The percolation threshold model explains phase transition behavior from the savanna-forest transition to the complement immune system to epidemics to morphogenesis to social networks.

Re: AlphaGenome: AI for better understanding the genome

#188

Post by cysteinechapel »

wespiser_2018 wrote: post #166
Kalanos wrote: post #164

The functional predictions related to "non-coding" variants are big here. Non-coding regions, referred to as the dark genome, produce regulatory non-coding RNA's that determine the level of gene expression in a given cell type. There are more regulatory RNA's than there are genes. Something like 75% of expression by volume is ncRNA.

It's possible that the "functional" aspect of non-coding RNA exists on a time scale much larger that what we can assay in a lab. The sort of "junk DNA/RNA" hypothesis: the ncRNA part of the genome is material that increases fitness during relative rare events where it's repurposed into something else. On a millions or billions of year time frame, the organisms with the flexibility of ncRNA would have an advantage, bu…

Such an advantage that is rare and across such long time scales would be so small on average that it would be effectively neutral. Natural selection can only really act on fitness advantages greater than on the order of the inverse of effective population size, which for large multicellular organisms such as animals, is low. Most of this is really just noisy transcription/binding/etc.

For example, we don't keep transposons in general because they're useful, which are almost half of our genomes, and are a major source of disruptive variation. They persist because we're just not very good at preventing them from spreading, we have some suppressive mechanisms but they don't work all the time, and there's a bit of an arms race between transposons and host. Nonetheless, they can occasionally provide variation that is beneficial.

Re: AlphaGenome: AI for better understanding the genome

#189

Post by cysteinechapel »

rattlesnakedave wrote: post #6
nextos wrote: post #3

I found it disappointing that they ignored one of the biggest problems in the field, i.e. distinguishing between causal and non-causal variants among highly correlated DNA loci. In genetics jargon, this is called fine mapping. Perhaps, this is something for the next version, but it is really important to design effective drugs that target key regulatory regions. One interesting example of such a problem and why it is…

Does this get us closer? Pretty uninformed but seems that better functional predictions make it easier to pick out which variants actually matter versus the ones just along for the ride. Step 2 probably is integrating this with proper statistical fine mapping methods?

There are existing frameworks for integrating functional and statistical fine mapping methods (e.g. polyfun + susie/finemap). They use annotation overlaps like epigenetic or conservation tracks but can be extended to variant effect predictions from models like this. They essentially modify the prior probability of a variant being causal from uniform to one that depends on the functional annotation.

Re: AlphaGenome: AI for better understanding the genome

#190

Post by cysteinechapel »

nextos wrote: post #7
rattlesnakedave wrote: post #6

Earlier quoted context omitted.

Does this get us closer? Pretty uninformed but seems that better functional predictions make it easier to pick out which variants actually matter versus the ones just along for the ride. Step 2 probably is integrating this with proper statistical fine mapping methods?

Yes, but it's not dramatically different from what is out there already. There is a concerning gap between prediction and causality. In problems, like this one, where lots of variables are highly correlated, prediction methods that only have an implicit notion of causality don't perform well. Right now, SOTA seems to use huge population data to infer causality within each linkage block of interest in the genome. Thes…

To push back a little, isn't there causality inherently in these sequence-to-function models, in the sense that causality must proceed in the direction of genetics -> predicted molecular function? And the genetic contribution to phenotype must pass through molecular function.
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