Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes

Fabris, Fabio, Palmer, Daniel, de Magalhaes, Joao Pedro ORCID: 0000-0002-6363-2465 and Freitas, Alex A (2020) Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes. BRIEFINGS IN BIOINFORMATICS, 21 (3). pp. 803-814.

Text paper_final_v3.pdf - Author Accepted Manuscript Download (255kB)

Abstract

Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a 'background' set of genes. These enrichment methods, although based on rigorous statistical foundations, are not always the best single option to identify patterns in biological data. In many cases, one can also use classification algorithms from the machine-learning field. Unlike enrichment methods, classification algorithms are designed to maximize measures of predictive performance and are capable of analysing combinations of gene properties, instead of one property at a time. In practice, however, the majority of studies use either enrichment or classification methods (rather than both), and there is a lack of literature discussing the pros and cons of both types of method. The goal of this paper is to compare and contrast enrichment and classification methods, offering two contributions. First, we discuss the (to some extent complementary) advantages and disadvantages of both types of methods for identifying gene properties that discriminate between gene classes. Second, we provide a set of high-level recommendations for using enrichment and classification methods. Overall, by highlighting the strengths and the weaknesses of both types of methods we argue that both should be used in bioinformatics analyses.

Item Type:	Article
Uncontrolled Keywords:	machine learning, enrichment analysis, classification, statistical hypothesis testing
Depositing User:	Symplectic Admin
Date Deposited:	28 Mar 2019 16:05
Last Modified:	19 Jan 2023 00:55
DOI:	10.1093/bib/bbz028
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3035211