> Will be interesting to see how ML and CRISPR converge

I've been seeing similar comments a lot lately, and I don't understand why. As someone with a background in ML that is now studying biochemistry I'd be the first person to jump at something like that, but I'm just not seeing any big ML potential that directly has to do with CRISPR.

Is it just two hyped technologies being thrown in a pot? Is there something I'm missing?

As someone about to graduate with a PhD in biochemistry who has been programming for ~10 years and personally used CRISPR/Cas9 ... the parent comment gave me a good laugh.

That being said, I can see some applications to using ML to analyze the results of CRISPR/Cas9 screens or better predict functional editing sites - but that's not exactly exclusive to Cas9.

Exactly what I had in mind. ML has its place in bioinformatics (and is being applied to a huge amount of sub-problems there), but it almost always is an incremental improvement for that niche rather than transforming the whole landscape.

I'm talking precisely about what this guy worked on - https://news.ycombinator.com/item?id=23540564

ML provides predictive capabilities over large fugly datasets. CRISPR gives us the ability to test those predictions. These are both essentially commodity technologies at this point and not everyone is going to take a disciplined approach to experimentation.

I know you what you're missing. You gotta put AI in the Blockchain before adding CRISPER and applying for funding.

EDIT: And don't forget Kubernetes.

It's not complete though until someone rewrites the whole thing from the ground up entirely in rust and declares it now safe to actually use.

Serverless Gene editing!

CRISPRAAS

You laugh but...

https://www.genscript.com/crispr-services.html

https://canopybiosciences.com/custom-cell-line-engineering-2...

https://www.reprocell.com/gene-editing-services-i174

etc. etc.

This guy lifestreams some really interesting work - https://www.youtube.com/user/TheChemlife/videos

I've only heard of the two topics seriously discussed together in the context of directed evolution. Instead of using radiation to increase the rate of mutation, use (semi) randomized snippets injected via CRISPR with a feedback loop that includes your optimization function (i.e. increasing temperature range for a protein).

The conclusion was that the data would be far too sparse for the algorithm to really learn anything.

My Ph.D. work has used machine learning to model CRISPR editing outcomes, see https://www.maxwshen.com/

Remember that CRISPR is just an editing technique, and we have the technical capabilities to synthesize genomes. To get to cellular automata, I think there needs to be much more focus on building rather than tweaking.

Though tweaking is very nice for eukaryotes, where we have no idea how to build and need to learn from small edits.

> we have the technical capabilities to synthesize genomes

Of very limited size (for now).

> Will be interesting to see how ML and CRISPR converge.

don't forget blockchain