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Provide equivalencies for phenotype terms #334

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caufieldjh opened this issue Feb 13, 2024 · 6 comments
Open

Provide equivalencies for phenotype terms #334

caufieldjh opened this issue Feb 13, 2024 · 6 comments
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enhancement New feature or request

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@caufieldjh
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From @matentzn at today's Monarch Huddle:

Wishlist to OntoGPT team: we need a scalable solution to suggestion EQs for phenotype ontologies.

@matentzn
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Pattern library: https://github.com/obophenotype/upheno/tree/master/src/patterns/dosdp-patterns

Some matches: https://github.com/obophenotype/upheno-dev/tree/master/src/curation/pattern-matches

which contains

https://github.com/obophenotype/upheno-dev/blob/master/src/curation/pattern-matches/hp/abnormalMorphologyOfAnatomicalEntity.tsv

The goal would be to:

  1. Use Phenio KG, patterns and known matches (potentially) as background knowledge
  2. generate tables like this https://github.com/obophenotype/upheno-dev/blob/master/src/curation/pattern-matches/hp/abnormalMorphologyOfAnatomicalEntity.tsv for all phenotypes that dont yet have one

something like that, didnt think this through

@caufieldjh
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caufieldjh commented Feb 13, 2024

Does this idea also include leveraging the LLM's ability to simulate inference of anatomical relationships, like "zebrafish don't have a jugular vein" - though that's also likely to be hallucination-prone

@caufieldjh caufieldjh added the enhancement New feature or request label Feb 13, 2024
@matentzn
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I would hope so! But I didnt think that far. SInce the input are the existing phenotype terms, we dont need to worry that we feed terms that do not exist.

@cmungall
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cmungall commented Feb 13, 2024 via email

@matentzn
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No I did not! I put it on our Agenda.

@cmungall
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Table 4:

image

Unfortunately the dumb ontology importer I wrote for curategpt doesn't support subq (have I mentioned how much I hate subq?) so mp/hp are exempt from this analysis, but OBA gives you an example at the extreme end of postcomp.

One major limitation of the analysis is that we limited things to new terms to avoid test data leakage. But for some ontologies like go only a handful of new terms had ldefs, so the sample size is very small here and likely biased towards "easy" cases hence the somewhat contradictory better performance on GO than on a patternized ontology

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