Name
Title | ||
|---|---|---|
BioKEEN: A library for learning and evaluating biological knowledge graph embeddings. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/btz117 | BIOINFORMATICS |
Field | DocType | Volume |
Command-line interface,Knowledge graph,Programming language,Biology,MIT License,Genetics,Hierarchy,Python (programming language),Software ecosystem | Journal | 35 |
Issue | ISSN | Citations |
18 | 1367-4803 | 1 |
PageRank | References | Authors |
0.35 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehdi Ali | 1 | 2 | 2.73 |
| Hoyt Charles Tapley | 2 | 8 | 3.62 |
| Daniel Domingo-Fernández | 3 | 9 | 5.48 |
| Jens Lehmann | 4 | 5375 | 355.08 |
| Hajira Jabeen | 5 | 67 | 10.58 |