Anna Breit1, Simon Ott1, Asan Agibetov1, Matthias Samwald1. 1. Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna 1090, Austria.
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.
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.
Authors: Daniel Domingo-Fernández; Yojana Gadiya; Abhishek Patel; Sarah Mubeen; Daniel Rivas-Barragan; Chris W Diana; Biswapriya B Misra; David Healey; Joe Rokicki; Viswa Colluru Journal: PLoS Comput Biol Date: 2022-02-25 Impact factor: 4.475