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

, , , , and . PLoS Comput. Biol., (2020)

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Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy., , , , , and . Fuzzy Sets Syst., (August 2023)Describing Sequential Association Patterns from Longitudinal Microarray Data Sets in Humans., , , , and . IWBBIO (2), volume 11466 of Lecture Notes in Computer Science, page 318-329. Springer, (2019)Human Multi-omics Data Pre-processing for Predictive Purposes Using Machine Learning: A Case Study in Childhood Obesity., , , , , , and . IWBBIO (2), volume 13347 of Lecture Notes in Computer Science, page 359-374. Springer, (2022)eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research., , , , and . PLoS Comput. Biol., (2020)Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity., , , , , , , , , and . Comput. Biol. Medicine, (September 2023)Gene Expression Profiles of Visceral and Subcutaneous Adipose Tissues in Children with Overweight or Obesity: The KIDADIPOSEQ Project., , , , , , , , , and . IWBBIO (1), volume 13346 of Lecture Notes in Computer Science, page 42-46. Springer, (2022)