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ethanfetaya / NRI

Neural relational inference for interacting systems - pytorch
MIT License
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relational inference in dynamic systems between different attributes #16

Open djimenezsanchez opened 5 years ago

djimenezsanchez commented 5 years ago

Hello,

I have read the paper and the code and I'm fascinated about this tool and their possible applications.

In my biological set-up I have different objects from which I want to create an interaction graph. Unfortunately, not all biological objects have the same number of attributes, e.g. fibrines have defined their morphometry but not their phenotype, and cells have defined their phenotype but not their morphology. I would like to know if there is any relation between them.

I have thought about creating an attribute vector containing all the features that are available. Following the example: fibrines would have a vector of 2 attributes with their morphometry leaving their phenotype undefined (using zeros or random numbers), and cells will have their phenotype defined leaving the morphometry undefined.

Can you give me any suggestions about this approach based on your experience?

Thank you, Daniel Jiménez.

fatcatZF commented 2 years ago

I think you can use a mask for the feature vectors; i.e., the mask has the same length as the feature vector, 1 denotes the known features and 0 denotes the unknown features.