BioKEEN: a library for learning and evaluating biological knowledge graph embeddings. Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Ali, Mehdi; Berrendorf, Max; Hoyt, Charles Tapley; Vermue, Laurent; Sharifzadeh, Sahand; Tresp, Volker; Lehmann, Jens: PyKEEN 1.0: a Python library for training and evaluating knowledge graph embeddings (2021)
- Yu, Shih-Yuan; Chhetri, Sujit Rokka; Canedo, Arquimedes; Goyal, Palash; Faruque, Mohammad Abdullah Al: Pykg2vec: a Python library for knowledge graph embedding (2021)
- Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann: PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Emebddings (2020) arXiv