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A potentially fun analysis would be to evaluate how the different moa/target annotations updated over time (in different CLUE drugs/samples versions) influence moa/target recall.
Essentially, we would setup an eval framework (I imagine there is a traditional moa/target recall eval) where we use the same input profiles and alter the moa/target information as they have been updated over time.
If we see improvement over time this tells us that annotations are improving and, potentially, that there is even more room to improve categorization.
The text was updated successfully, but these errors were encountered:
We can do this eval for both Cell Painting and L1000 for the same profile perturbations.
Of course, how the annotations are being updated is extremely important here (i.e. if, for example, the annotations are updated using L1000 assays, then we'd expect moa/target annotations to improve more using L1000 data (and vice versa))
cc @shntnu - I can imagine that this analysis could find a home somewhere in the eval "offshoot" analysis paper (discussed in the context of Pooled CP) for the Drug Repurposing data
gwaybio
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May 22, 2020
A potentially fun analysis would be to evaluate how the different moa/target annotations updated over time (in different CLUE drugs/samples versions) influence moa/target recall.
Essentially, we would setup an eval framework (I imagine there is a traditional moa/target recall eval) where we use the same input profiles and alter the moa/target information as they have been updated over time.
If we see improvement over time this tells us that annotations are improving and, potentially, that there is even more room to improve categorization.
The text was updated successfully, but these errors were encountered: