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I worked at a biomedical informatics shop that did this very thing by AI/ML 10 years ago. Essentially: automated metanalysis.


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You reminded me of something that does exactly this for machine learning. I posted it on the front page of HN (Metacademy).

We are doing similar work at GenHealth.ai and getting sota results on some evals (not yet published). Our approach is very different from LLMs in that we are using a medical coding vocabulary and we are training transformers on actual patient histories. We have an API if anyone here wants to build on it. Oh and we are hiring

There was some interesting work done on using ML for autovectorisation. Pldi keynote from a year or two ago iirc.

Maybe we could model this into a Machine Learning automation process.

This is pretty much my exact experience in doing AI/ML research.

This is impressive. Wonderful work to OP.

I’m currently working with Digistain (S21) and we’re using AI to predict breast (and eventually other) cancer recurrence.

The tests are performed using infrared spectroscopy to measure protein synthesis and then fed into AI in order to make proper measurements and predictions.

We’ve shown we’re able to predict better than any other known method and are beginning our partnership and rollout to many hospitals around the world.


Very neat. It sounds like they're curating a dataset ripe for machine learning.

Absolutely, and I'm glad you linked them! Cool to read about applications of ML in healthcare.

Interestingly I too worked on MDM systems about ten years ago, when I was at IBM Research. Ironically, one of my first ideas for applying machine learning was in de-duplication of data in an MDM server. However the technology was a bit too primitive back in 2010 and the project was a hard sell so it was abandoned.

I work in data science for a pharma/chemical company. Broadly speaking, our team is applying machine learning to chemistry and biology-related problems. Those are usually falsifiable and can and will be validated through lab experiments.

The main problem here is that experiments tend to be expensive - depending on the problem a single data point can easily cost from $100+ (sample preparation and measurements) to $100k+ (e.g. synthesis of a new compound). So our datasets are often small, and there is some barrier for lab colleagues to trust/try out some new ML model vs their status quo.

But it is quite rewarding when it works and one also gets to interact with people from different disciplines on all sorts of interesting problems :)


They made it specifically for machine learning

sounds like SOTA ml research

Sounds interesting. Do you have any links to information about how they use machine learning?

Sounds like a hell of a lab! What you're doing on it, machine learning ?

Speaking of which, check out Michael I. Jordans work on Probabilistic Graphical Models https://www.google.com/search?q=michael+i+jordan+probalistic...

Mentor to Andrew Ng, former head of Google AI, Baidu and a few other things. https://en.wikipedia.org/wiki/Michael_I._Jordan

Saira, Mina and David worked on some interesting stuff related to using ML/AI in extending human life span, nematodes a while back. Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span - Blei DM, Franks K, Jordan MI, Mian IS. - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533868


Sounds like an opportunity for machine learning. Anyone want to write an AI BOFH?

I do machine learning for aberration correction in the transmission electron microscope. We are not the hippest ML lab (META, Deepmind.. ), but its super cool stuff.

- Im also interested in learning what machine algorithm you used.

This is awesome! I'm sensing some Machine Learning applications here.
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