Knowledge-Based Biomedical Data Science

Abstract

Knowledge-based biomedical data science 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 recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.

Keywords: Semantic Web; knowledge discovery; knowledge graph; knowledge graph embeddings; natural language processing; ontology.

Figure 1

An example of a knowledge representation for building a biomedical knowledge graph. Boxes represent different types of data, which are drawn from ontologies and other sources of linked open data. Boxes are connected by directed edges and represent semantically and biologically meaningful relationships.

Figure 2

Paper selection process outline. Combining the results from PubMed and Google Scholar queries, we narrowed down the list of papers using a two-step process. First, we performed a quick review to reduce the initial number of papers. Then, we closely inspected each paper, which helped us to arrive at the final set of 83 papers.