RT Journal Article
T1 eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research
A1 Anguita-Ruiz, Augusto
A1 Segura-Delgado, Alberto
A1 Alcalá, Rafael
A1 Aguilera, Concepción M.
A1 Alcalá-Fdez, Jesús
K1 Artificial Intelligence
K1 Gene expression
K1 Obesity
K1 Knowledge
K1 Mining
K1 Transcriptome
K1 Inteligencia artificial
K1 Expresión génica
K1 Obesidad
K1 Conocimiento
K1 Minería de datos
K1 Transcriptoma
AB 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.
PB Public Library of Science
YR 2020
FD 2020-04-10
LK http://hdl.handle.net/10668/3873
UL http://hdl.handle.net/10668/3873
LA en
NO Anguita-Ruiz A, Segura-Delgado A, Alcalá R, Aguilera CM, Alcalá-Fdez J. eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research. PLoS Comput Biol. 2020 Apr 10;16(4):e1007792
DS RISalud
RD Apr 9, 2025