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.
@article{arxiv.1910.06710,
title = {Knowledge-based Biomedical Data Science 2019},
author = {Tiffany J. Callahan and Harrison Pielke-Lombardo and Ignacio J. Tripodi and Lawrence E. Hunter},
journal= {arXiv preprint arXiv:1910.06710},
year = {2022}
}
Comments
Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 tables