eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research
Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
Explainable artificial intelligence to predict and identify prostate cancer tissue by gene expression
Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity
Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy
Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension
Mining high average-utility sequential rules to identify high-utility gene expression sequences in longitudinal human studies
Omics Approaches in Adipose Tissue and Skeletal Muscle Addressing the Role of Extracellular Matrix in Obesity and Metabolic Dysfunction
