@misc{10481/62400,
year = {2020},
month = {4},
url = {http://hdl.handle.net/10481/62400},
abstract = {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, knowledgeextraction 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.},
organization = {This work was supported by the Mapfre
Foundation (“Research grants by Ignacio H. de
Larramendi 2017”) and by the Regional
Government of Andalusia ("Plan Andaluz de
investigación, desarrollo e innovación (2018), P18-
RT-2248"). The authors also acknowledge the
Institute of Health Carlos III for personal funding:
Contratos i-PFIS: doctorados IIS-empresa en
ciencias y tecnologías de la salud de la
convocatoria 2017 de la Acción Estratégica en
Salud 2013–2016, Project number: IFI17/00048.
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.},
publisher = {Benjamin Althouse, Institute for Disease Modeling, UNITED STATES},
title = {eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research},
doi = {https://doi.org/10.1371/journal.pcbi.1007792},
author = {Anguita-Ruiz, Augusto and Segura Delgado, Alberto and Alcalá Fernández, Rafael and Aguilera García, Concepción María and Alcalá Fernández, Jesús},
}