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Universal medical image segmentation foundation model trained on 1.57M image-mask pairs across 10 imaging modalities and 30+ cancer types (Nature Communications 2024)

Provides functionality for producing geometric representations of protein and RNA structures, and biological interaction networks.

Babelon is a simple standard for managing ontology translations and language profiles. Profiles are managed as TSV files, see for example https://github.com/obophenotype/hpo-translations/tree/main/babelon. The goal of Babelon as a data model and vocabulary is to capture the minimum data required to capture important metadata such as confidence and precision of translation.

This ontology models classes and relationships describing deep learning networks, their component layers and activation functions, as well as potential biases.