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license: mit
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Random forest models trained on heat kernel signature features to predict the compartment target of a synapse on neuronal meshes.
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Method is described in more detail in [Pedigo et al. bioRxiv 2026](https://www.biorxiv.org/content/10.64898/2026.02.19.706834v1).
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license: mit
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tags:
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- neuroscience
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- connectomics
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Random forest models trained on heat kernel signature (HKS) features to predict the compartment target of a synapse on neuronal meshes.
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Method is described in more detail in [Pedigo et al. bioRxiv 2026](https://www.biorxiv.org/content/10.64898/2026.02.19.706834v1).
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The feature generation pipeline used here can be found in the [meshmash repo](https://github.com/bdpedigo/meshmash/blob/7395e0d8d3b0e3fb23069761961eb7055b7b9700/src/meshmash/pipeline.py#L389).
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The parameters for that function are in `parameters.toml`.
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`synapse_hks_model.joblib` takes in the 32 HKS features generated by that pipeline, as well as a column of the distance from each point to the postsynaptic nucleus.
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The model outputs predictions for {"spine", "shaft", "soma"} targets. This is the model used in the paper.
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`simple_hks_model.joblib` uses only the HKS features and outputs {"spine", "not_spine"} labels.
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