ArticlePLoS computational biology2020
eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Considerations on efforts needed to improve our understanding of the genetics of obesity.International journal of obesity (2005) · 2025Review
- Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics.Computational and structural biotechnology journal · 2025Review
- Omics Studies of Specialized Cells and Stem Cells under Microgravity Conditions.International journal of molecular sciences · 2024Review
- Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging.NPJ digital medicine · 2024Review
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.Artificial intelligence in medicine · 2023Review
- Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data.Genomics, proteomics & bioinformatics · 2022Article
- Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques.International journal of environmental research and public health · 2022Article
- Interpretable machine learning for genomics.Human genetics · 2022Article
- An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.PLOS digital health · 2022Article
- Weakly-supervised deep learning for ultrasound diagnosis of breast cancer.Scientific reports · 2021Article
- Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Review
- Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence.Cancers · 2021Review
- Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.PloS one · 2021Article
- Machine learning in postgenomic biology and personalized medicine.Wiley interdisciplinary reviews. Data mining and knowledge discoveryArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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, 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.
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