Incorporating biological structure into machine learning models in biomedicine. (June 2020)

• Record Type: Journal Article
• Authors: Crawford, Jake; Greene, Casey S
• Is Part Of: Current opinion in biotechnology. Volume 63(2020)
• Page Start: 126
• Page End: 134
• Publication Date: 2020-06
• Subjects: Biotechnology -- Periodicals; 660.6
• DOI: 10.1016/j.copbio.2019.12.021
• Languages: English
• ISSNs: 0958-1669

Abstract

Graphical abstract: Schematic showing the main categories of models incorporating structured biological data covered in this review. The first panel shows an example of a model operating on sequence data, the second panel shows a model in which dimension reduction is influenced by the connections in a gene network, and the third panel shows a neural network with structure constrained by a phylogeny or ontology. The 'x' values in the data tables represent gene expression measurements. Abstract : In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexpression networks, ontologies, and phylogenetic trees. We highlight recent examples of machine learning models that use structure to constrain model architecture or incorporate structured data into model training. For machine learning in biomedicine, where sample size is limited and model interpretability is crucial, incorporating prior knowledge in the form of structured data can be particularly useful. The area of research would benefit from performant open source implementations and independent benchmarking efforts.