hybrid-doc-relevance-training

This software corresponds to Hybrid-doc-relevance-training which provides a set of hybrid embedding approaches for literature-based document-to-document similarity, leveraging the RELISH corpus along with integrating semantic understanding. It implements three distinct algorithms: pre-annotation, post-annotation, and post-reduction annotation, utilizing models such as Doc2Vec, Word2Vec, and FastText. The software combines these models with ontology-based background knowledge to improve document similarity, relevance, and recommendation. Detailed documentation is provided, including input data preprocessing and execution instructions for easy integration and use. This work used deNBI resources and therefore was supported by the de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) and ELIXIR-DE (Forschungszentrum Jülich and W-de.NBI-001, W-de.NBI-004, W-de.NBI-008, W-de.NBI-010, W-de.NBI-013, W-de.NBI-014, W-de.NBI-016, W-de.NBI-022).