Knowledge-based Biomedical Data Science 2019

TL;DR

This paper surveys recent advances in knowledge-based biomedical data science, focusing on knowledge graphs, machine learning integration, natural language processing, and expanding applications in diverse biomedical domains.

Contribution

It provides a comprehensive overview of recent developments in systems using formal knowledge representations for biomedical data science, highlighting new applications and methodologies.

Findings

Progress in integrating knowledge graphs with machine learning.

Advances in natural language processing for biomedical data.

Expansion of knowledge-based approaches to new biomedical domains.

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.