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diseasy

Ideas

  • Compare text vs. semantic similarity
  • Bake-off various language distance metrics
  • Various method of distance too
  • Compare to random

Can you use text comparison methods to find similarities between human diseases and zebrafish phenotypes?

Or do you need a very custom mapping via ontologies? The original design was to do something like this. Good idea to first do a bunch of comparisons and then determine if you need to develop something new.


  • Download a bunch of python-based text comparision libraries
  • Start some simple bake-offs
  • Figure out how to do the random model
  • Which are our gold standards?

Q: What does failure look like? A: Random is indistinguishable from real diseases

Q: What does success look like? A: Gold standards are found


Clustering

Compare human diseases vs. human diseases Compare zf phenotypes vs zf phenotypes


textcompare1.py

install conda pip3 install nltk scikit-learn transformers torch fasttext

curl https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz --output cc.en.300.bin


textcompare2.py

pip3 install -U sentence-transformers

works


textcompare3.py

pip3 install tensorflow tensorflow_hub

works

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