This is interesting as a sustained exercise in formalizing a difficult domain. The lesson seems to be that it's essential to ensure computability, but usabilit

This is interesting as a sustained exercise in formalizing a difficult domain. The lesson seems to be that it's essential to ensure computability, but usability and transition costs dominate the adoption decision, making formal methods rarely popular. A lot of the actual solutions boil down to ETL-style data conversions and query language extensions. The opposite of "move fast and break things", the sharing of genomic and ontology data place a high premium on shared standards, making progress relatively arduous. Worse, much of the data is semantically complex and not normalized, with slight variants supported by different vendors and thus lots of work at the application level to normalize data for inter-operation. Since the output is e.g., a potential cancer diagnosis or drug selection, the stakes for mistakes are high. Normally these kinds of coordination costs create an opportunity for an integrated player. Illumina has tried to extend its near-monopoly on short-read sequencing into downstream software and upstream robots and reagents (and across domains from medicine to synthetic biology), but the availability of public data and tooling from universities (esp. UCSC) and places like LBL has enabled other market player to resist and collaborate without integrating. Some of that is driven by the US, but a lot is European, with Qiagen (and here with the monarch initiative even Bosch) and other pharma companies. It's a history worth considering now as AI companies begin the land/gold rush for semantic data. Who would pay for a relatively independent career like Chris' in AI?