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We want to add a table with all the cross-phenotype matches based on graph-machine learning.
Input graph: phenio
Learning set:
known equivalencies?
An interesting negative edge set (known false predications) could be anything within an prefix space (MP-MP), but I think this may screw up the algorithm
Output:
a table of predicted cross-species equivalencies (cut off at some threshold of your choosing)
a table of known equivalencies (training set) which are not predicted well by the final model (for QC purposes, and because it is interesting)
The equivalencies we are interested in are those where the subject and the object are from different prefix spaces. The prefix spaces of interest are: HP, MP, ZP, XPO, WBPhenotype, DPO (FBcv), PLANP, FYPO, DDPHENO, MGPO (the order is from most to least important for the time being).
The text was updated successfully, but these errors were encountered:
We want to add a table with all the cross-phenotype matches based on graph-machine learning.
Input graph: phenio
Learning set:
Output:
The equivalencies we are interested in are those where the subject and the object are from different prefix spaces. The prefix spaces of interest are: HP, MP, ZP, XPO, WBPhenotype, DPO (FBcv), PLANP, FYPO, DDPHENO, MGPO (the order is from most to least important for the time being).
The text was updated successfully, but these errors were encountered: