# Knowledge-based Biomedical Data Science 2019

arXiv: https://arxiv.org/abs/1910.06710 · HTML: https://mlsanity.com/paper/1910.06710/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [MolFM: A Multimodal Molecular Foundation Model](https://mlsanity.com/paper/2307.09484/) (2023)
- [A scoping review on multimodal deep learning in biomedical images and texts](https://mlsanity.com/paper/2307.07362/) (2023)
- [An Open-Source Knowledge Graph Ecosystem for the Life Sciences](https://mlsanity.com/paper/2307.05727/) (2023)
- [Knowledge Engineering using Large Language Models](https://mlsanity.com/paper/2310.00637/) (2023)
- [Domain-informed graph neural networks: a quantum chemistry case study](https://mlsanity.com/paper/2208.11934/) (2022)
- [Medical Pathologies Prediction : Systematic Review and Proposed Approach](https://mlsanity.com/paper/2304.00311/) (2023)
- [Agents for Everyone: A Workshop Framework for Building Agentic AI Capabilities in a Distributed Curation Community](https://mlsanity.com/paper/2608.27675/) (2026)

## Abstract (arXiv)

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.