Anguita-Ruiz, AugustoSegura-Delgado, AlbertoAlcalá, RafaelAguilera, Concepción M.Alcalá-Fdez, Jesús2022-08-042022-08-042020-04-10Anguita-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):e1007792http://hdl.handle.net/10668/3873Until 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.enAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Artificial IntelligenceGene expressionObesityKnowledgeMiningTranscriptomeInteligencia artificialExpresión génicaObesidadConocimientoMinería de datosTranscriptomaMedical Subject Headings::Information Science::Information Science::Computing Methodologies::AlgorithmsMedical Subject Headings::Information Science::Information Science::Computing Methodologies::Artificial IntelligenceMedical Subject Headings::Disciplines and Occupations::Natural Science Disciplines::Biological Science Disciplines::Biology::Computational BiologyMedical Subject Headings::Information Science::Information Science::Medical Informatics::Medical Informatics Applications::Information Storage and Retrieval::Data MiningMedical Subject Headings::Information Science::Information Science::Medical Informatics::Medical Informatics Applications::Information Systems::Databases as Topic::Databases, Factual::Databases, GeneticMedical Subject Headings::Phenomena and Processes::Genetic Phenomena::Genetic ProcessesMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Genetic Techniques::Gene Expression ProfilingMedical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::HumansMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal StudiesMedical Subject Headings::Diseases::Nutritional and Metabolic Diseases::Nutrition Disorders::Overnutrition::ObesityMedical Subject Headings::Information Science::Information Science::Computing Methodologies::SoftwareMedical Subject Headings::Phenomena and Processes::Genetic Phenomena::Genetic Structures::Transcriptomeresearch article32275707Acceso abierto10.1371/journal.pcbi.10077921553-7358PMC7176286