Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences, respectively, to GO terms and protein entities. ProteinKG65 is mainly dedicated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. We also illustrate the potential applications of ProteinKG65 with a prototype. Our dataset can be downloaded at https://w3id.org/proteinkg65.
@article{arxiv.2207.10080,
title = {Multi-modal Protein Knowledge Graph Construction and Applications},
author = {Siyuan Cheng and Xiaozhuan Liang and Zhen Bi and Huajun Chen and Ningyu Zhang},
journal= {arXiv preprint arXiv:2207.10080},
year = {2022}
}
Comments
Accepted by AAAI 2023 (Student Abstract). Dataset available in https://zjunlp.github.io/project/ProteinKG65/