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.
|Original language||English (US)|
|Number of pages||3|
|State||Published - Feb 15 2019|
Bibliographical noteKAUST Repository Item: Exported on 2021-03-12
Acknowledgements: We thank our partners from the Bio2Vec, MLwin and SimpleML projects for their assistance.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computational Mathematics
- Molecular Biology
- Statistics and Probability
- Computer Science Applications