Knowledge-based Biomedical Data Science 2019
arXiv:1910.06710 · doi:10.1146/annurev-biodatasci-010820-091627
Abstract
Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.
arXiv commentManuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 tables
References in corpus (6)
- Link Prediction in Complex Networks: A Survey
- A Review of Relational Machine Learning for Knowledge Graphs
- Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
- Neuro-symbolic representation learning on biological knowledge graphs
- Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs
- Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis