mediKanren

A theorem prover for the medical literature: https://github.com/webyrd/mediKanren http://minikanren.org/workshop/2020/minikanren-2020-paper7.p... Not prolog though. But gives an idea about the goals behind the classification of science papers.

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

IMO, you overstate the issue of syntax. As a hobbyist in both programming and math, Coq's syntax has never been the reason I failed to complete a proof. But perhaps I'm just too dumb and my difficulties lie elsewhere, so that's just my 2c. I think there's room for a spectrum of theorem provers made for academic pure mathematicians, industry programmers and everything in between. Those should perhaps not have identical syntax, neither should they have the same goals. To support my point, here's an example of an exotic theorem prover: https://github.com/webyrd/mediKanren It is aimed at medical researchers, and computes proofs about the medical literature, no less! This is a very different system and audience than which you are thinking about, but it's still a theorem prover.

Specific plug for Will Byrd's mediKanren and Matt Might's group in general they are the real deal. (source: I have worked for other deals). For more just start learning the tools... I have not checked in on them for years now but "BioStars Handbook" was up & coming [] https://github.com/webyrd/mediKanren [] https://biostar.myshopify.com/

This question is tangential, but why do you think it is that there are so many people in the "new philanthropist" class who do have the money to fund such research privately, but instead partner with the big pharma companies? If the costs for these types of solutions can be SO much lower, it surprises me that there isn't a more robust subculture working on it with money they've made when their last tech startups went public. Almost everybody knows somebody with a rare or expensive-to-treat disease, right? Is it just too early? Are we only just learning how to figure out exactly which mutations cause exactly the wrong protein production errors in the cells, and then can assess the effects of off-patent generics to treat them? Or is there more to it than that? I only learned about Medikanren[0] a couple weeks ago, and since this isn't my fi

These guys at UAB [1] are doing amazing things with evidence-based drug discovery and repurposing, using logic programming [2]. Fund them! They're the future! [1] https://www.uab.edu/medicine/pmi/ [2] https://github.com/webyrd/mediKanren

I recently got my full genome sequenced via a website called nebula.org. I don't have any backgrounds in genetics but I was playing around with the raw data with some github apps. Thanks so much for this - I may be able to link this app in to my custom data. That would be so awesome if we could hook this up to personal genetics results. Even people who aren't sick do have mutations and it would be so cool seeing AI running some kind of search on their personal genome and suggesting a drug for that! Here's the link to the github app they mentioned: https://github.com/webyrd/mediKanren

> Proof-of-concept for reasoning over medical knowledge graphs, using miniKanren + heuristics + indexing. https://github.com/webyrd/mediKanren

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,

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.

It was fun to read this because I happened to study at Indiana University where most of the work on *Kanren happened. If anyone is interested in practical applications, Will Byrd (mentioned in the story) went on to work with Matt Might (HN's favorite CS-prof-turned-doctor). One of the products of that was mediKanren[0] which operates over medical research. There was some publicity around mediKanren last year.[1] [0] https://github.com/webyrd/mediKanren [1] https://www.uab.edu/news/health/item/10703-diagnosis-in-2-12...

Github link: https://github.com/webyrd/mediKanren

Here is mediKanren (from the same authors): https://github.com/webyrd/mediKanren It's used for automated search through medical databases for drug discovery: https://www.uab.edu/mix/stories/a-high-speed-dr-house-for-me... Here is the Gerrit code review system: https://www.gerritcodereview.com/ It allows you to specify complex rules for workflows, who may sign off on what patches etc. Those rules are written in Prolog, but I guess they might as well be written in some other logic language. Here is (or rather, will be) Gigahorse, an Ethereum smart contract decompiler: https://2019.icse-conferences.org/event/icse-2019-technical-... The abstract doesn't say what "declarative, logic-based specification" is used in the implementation, but I know that two of the authors do a lot of work in Datalog, so it's Datalog.