mediKanren VS sketching

Not really production, but probably THE most impressive biomedicine research work I've seen (and I'm an academic MD):

https://github.com/webyrd/mediKanren

This is a FOL theorem prover that uses medical research articles as terms. They use it to do genetics and drug repurposing metaresearch. It's like the wet dream of all the biomed machine learning fanboys out there, except that:

1. it's not machine learning

and

2. it really works

And wow, he uses logic programming to deduce a diagnostic from the facts https://github.com/webyrd/mediKanren .. and used to find out what his son had https://www.statnews.com/2019/07/25/ai-expert-writing-code-save-son/

Hi Kamaal!

I know Cisco is using core.logic, which is David Nolen's Clojure variant of miniKanren, in their ThreatGrid product. I think the Enterprisey uses of mediKanren are a bit different than the purely relational programming that I find most interesting, though.

Having said that, we are now on our second generation of mediKanren, which is software that performs reasoning over large biomedical knowledge graphs:

https://github.com/webyrd/mediKanren/tree/master/medikanren2

mediKanren is being developed by the Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham (HKPMI). HKPMI is run by Matt Might, who you may know from his work on abstract interpretation and parsing with derivatives, or from his more recent work on precision medicine. mediKanren is part of the NIH NCATS Biomedical Data Translator Project, and is funded by NCATS:

https://ncats.nih.gov/translator

Greg Rosenblatt, who sped up Barliman's relational interpreter many order of magnitude, has been hacking on dbKanren, which augments miniKanren with automatic goal reordering, stratified queries/aggregation, a graph database engine, and many other goodies. dbKanren is the heart of mediKanren 2.

I can imagine co-writing a book on mediKanren 2, and its uses for precision medicine...

Cheers,

--Will

The Precision Medicine Institute that I now run produces mediKanren: https://github.com/webyrd/mediKanren

It's an open source logical reasoning engine (read: 1960's AI) for drug repurposing that we deploy routinely to help patients.

There is always a need for better relationalization of biological data sets that feed such tools too.

For example, SemMedDB is really showing its age for NLP of the scientific literature and yet it is still astonishingly useful for helping patients even as is.

If like me scheme is more your thing I discovered this library for Racket today:

https://github.com/soegaard/sketching

Some ideas to get you started; * Sketching - like Processing but better ! Look at the repo for a brief introduction, with links to the full documentation and examples: https://github.com/soegaard/sketching * Animate an AOC solution * use one of the functional picture libraries to create an Escher tessellation Racket functional picture libraries (pict or 2htdp/image). More inspiration from Conal Elliot Functional Images * Use Graphite to visualise some data