NCATSTranslator / Feedback

A repo for tracking gaps in Translator data and finding ways to fill them.
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Convergent phenotyping #503

Open karafecho opened 10 months ago

karafecho commented 10 months ago

In a recent meeting with Tony Hickey, external SME, he provided feedback on the Translator UI related to what he refers to as "convergent phenotyping", which is essentially the idea of leveraging shared phenotypes to identify treatments for rare disease, or drug repurposing for rare disease based on common phenotypes or common diseases with established treatments.

One example that Tony provided is that many rare diseases are associated with seizures. What are the commonalities? Are there treatments/therapies that might target those commonalities?

One of the Translator MVP1 results that Tony seemed to like was for an inferred response that included 26 paths linking a drug to a rare disease by way of one or more phenotypes. He suggested that it would be helpful for users to see a cross-section of genes among those paths, i.e., grouping the paths by intermediate genes.

Other specific (somewhat redundant) suggestions included the following, some of which have been suggested by other SMEs:

Note that the answer coalescer / graph enrichment feature that ARAGORN developed may address at least some of Tony's comments and desired features.

gprice1129 commented 9 months ago

@karafecho this definitely requires some discussion and breakdown.

mbrush commented 9 months ago

I had a conversation with @cmungall at the Relay that may be pertinent to this issue, about incorporating results from the Monarch phenotypic similarity tool into Translator.

Monarch has a tool that can quantify the similarity between two entities (e.g. two different Diseases, a Gene and a Disease) based on the similarity of their phenotype profiles (the set of all phenotypes associated with them, in KBs such as HPOA). See the slide here for an example that shows a phenotypic comparison between two rare disease patients with related phenotypic profiles.

In theory, we could use this Monarch tool to pre-compute phenotype similarity between any two diseases, or disease-gene pairs - and define a score cutoff above which we create an 'is phenotypically similar to' edge between them. We could then stand up a KP to serve these edges for use by ARAs performing reasoning/prediction.

This could provide valuable input for algorithms looking for drug repurposing opportunities (if Drug X treats Disease Y, it may also treat phenotypically similar Disease Z). Longer term, we could think about how to more dynamically incorporate these types of tools from Monarch into Translator (e.g. a workflow to allow dynamic query of a user input set of phenotypes to find phenotypically-similar diseases, and look at known treatments for those diseases).

Mathelab commented 9 months ago

Quick note also that Gwenlyn also has a hashing algorithm that can compute similarity between any entity types. Not sure how to tag her here but this is very relevant as well.

karafecho commented 9 months ago

@gglusman : See above.

karafecho commented 9 months ago

@gprice1129 : Apologies for packing this ticket with multiple issues. Sometimes that's the easiest way to make sure I've captured and documented issues that are generated during a SME engagement session, but I recognize the downstream impacts. I guess I would say that the main issue here is the one that is receiving the most attention, namely, the general concept of "convergent phenotyping" and leveraging shared phenotypes to identify drug treatments for rare disease. The other issues, e.g., explorer function, are largely covered by other tickets, I think.

Hope this helps ...

gprice1129 commented 9 months ago

@karafecho I think its perfectly reasonable to pack one ticket with several issues, but at some point a breakdown of that ticket needs to happen to spawn actionable tickets. When that happens we can close this one.

sierra-moxon commented 5 months ago

TAQA task: break down this issue.