ML Anthology

Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)

Siyuan Cheng, Xiaozhuan Liang, Zhen Bi, Huajun Chen, Ningyu Zhang
AAAI 2023 pp. 16190-16191
doi:10.1609/AAAI.V37I13.26955 /aaai/2023/cheng2023aaai-multi/

Abstract

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.

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Cite

Text

Cheng et al. "Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26955

Markdown

[Cheng et al. "Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/cheng2023aaai-multi/) doi:10.1609/AAAI.V37I13.26955

BibTeX

@inproceedings{cheng2023aaai-multi,
  title     = {{Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)}},
  author    = {Cheng, Siyuan and Liang, Xiaozhuan and Bi, Zhen and Chen, Huajun and Zhang, Ningyu},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16190-16191},
  doi       = {10.1609/AAAI.V37I13.26955},
  url       = {https://mlanthology.org/aaai/2023/cheng2023aaai-multi/}
}