RT Journal Article
SR Electronic
T1 DOUBLER: Unified Representation Learning of Biological Entities and Documents for Predicting Protein–Disease Relationships
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.27.357202
DO 10.1101/2020.10.27.357202
A1 Sztyler, Timo
A1 Malone, Brandon
YR 2020
UL http://biorxiv.org/content/early/2020/10/27/2020.10.27.357202.abstract
AB Motivation We propose a system that learns consistent representations of biological entities, such as proteins and diseases, based on a knowledge graph and additional data modalities, like structured annotations and free text describing the entities. In contrast to similar approaches, we explicitly incorporate the consistency of the representations into the learning process. In particular, we use these representations to identify novel proteins associated with diseases; these novel relationships could be used to prioritize protein targets for new drugs.Results We show that our approach outperforms state-of-the-art link prediction algorithms for predicting unknown protein–disease associations. Detailed analysis demonstrates that our approach is most beneficial when additional data modalities, such as free text, are informative.Availability Code and data are available at: https://github.com/nle-sztyler/research-doublerContact timo.sztyler{at}neclab.euSupplementary information Supplementary data are available at Bioinformatics online.Competing Interest StatementThe authors have declared no competing interest.