eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research

• Published in: PLoS computational biology Vol. 16; no. 4; p. e1007792
• Main Authors: Anguita-Ruiz, Augusto, Segura-Delgado, Alberto, Alcalá, Rafael, Aguilera, Concepción M., Alcalá-Fdez, Jesús
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Annotations; Artificial intelligence; Artificial Intelligence - trends; Biology and Life Sciences; Computational Biology - methods; Computer and Information Sciences; Data mining; Data Mining - methods; Data processing; Databases, Genetic; Design of experiments; Diet; Dynamic models; Experimental design; Explainable artificial intelligence; Gene expression; Gene Expression - genetics; Gene Expression Profiling - methods; Genomics; Humans; Identification and classification; Learning algorithms; Longitudinal Studies; Machine Learning; Medicine and Health Sciences; Model accuracy; Nutrition; Obesity; Obesity - genetics; Physical Sciences; Research and Analysis Methods; Software; Source code; Transcriptome - genetics; Spain
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007792

Until date, several machine learning approaches have been proposed for the dynamic modeling of temporal omics data. Although they have yielded impressive results in terms of model accuracy and predictive ability, most of these applications are based on "Black-box" algorithms and more interpretable models have been claimed by the research community. The recent eXplainable Artificial Intelligence (XAI) revolution offers a solution for this issue, were rule-based approaches are highly suitable for explanatory purposes. The further integration of the data mining process along with functional-annotation and pathway analyses is an additional way towards more explanatory and biologically soundness models. In this paper, we present a novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED). To illustrate the performance of our pipeline, we work on in vivo temporal GED collected within the course of a long-term dietary intervention in 57 subjects with obesity (GSE77962). As validation populations, we employ three independent datasets following the same experimental design. As a result, we validate primarily extracted gene patterns and prove the goodness of our strategy for the mining of biologically relevant gene-gene temporal relations. Our whole pipeline has been gathered under open-source software and could be easily extended to other human temporal GED applications.