cancervariants / cancervariants/therapy-normalization

Optimize normalized concept generation

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performance
Dominant language
Python
Stars
15
Forks
3
PR merge metrics
No merged PRs in 30d

Description

Brainstorming:

* Don't create new DB entries unless the new concept differs from the old one, and keep updates to a minimum
* Rather than scanning for all concept IDs, querying to build concept groups, and querying again to build the record itself, try to combine steps

Contributor guide

No contributing guide indexed for this repository

Research direction

No files, tests, or entry points are named. Start by locating normalized concept generation and its database writes, then compare the current queries and updates with the two optimization goals; done means avoiding unchanged concept entries and minimizing the query steps without changing the generated record.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
bioinformatics, databases
Issue type
Refactor
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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