OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

Author:

Breit Anna1,Ott Simon1,Agibetov Asan1,Samwald Matthias1ORCID

Affiliation:

1. Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna 1090, Austria

Abstract

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

European Union’s Horizon 2020 research and Innovation program

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference11 articles.

1. BioKEEN: a library for learning and evaluating biological knowledge graph embeddings;Ali;Bioinformatics,2019

2. Neuro-symbolic representation learning on biological knowledge graphs;Alshahrani;Bioinformatics,2017

3. Neural networks for link prediction in realistic biomedical graphs: a multidimensional evaluation of graph embedding-based approaches;Crichton;BMC Bioinformatics,2017

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