An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38136941", "kind": "science-record", "revision": 4, "content_hash": "sha256:c33105a8fb709a659d16ec0da3b0c06f35fc90a6af3ff3485a4bc8a0510d6b13", "created_at": "2026-11-01T19:20:04Z", "updated_at": "2026-11-01T22:53:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression.", "doi": "10.3390/genes14122119", "pmid": "38136941", "authors": ["Jesús María González-Martín", "Laura B Torres-Mata", "Sara Cazorla-Rivero", "Cristina Fernández-Santana", "Estrella Gómez-Bentolila", "Bernardino Clavo", "Francisco Rodríguez-Esparragón"], "journal": "Genes", "year": 2023, "abstract": "Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier \"GSE22309\". The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.", "claims": [{"id": "claim-method", "statement": "In this case, the linear models for the microarrays method, implemented in the “limma” package [18], were used to select differentially expressed genes. The comparisons between groups were DB vs. IR, DB vs. IS, and IR vs. IS. The adjusted p-value was calculated following Benjamini and Hochberg [19];", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38136941", "locator": "sections[2] \"2. Materials and Methods\" paragraph 2", "quote": "In this case, the linear models for the microarrays method, implemented in the “limma” package [18], were used to select differentially expressed genes. The comparisons between groups were DB vs. IR, DB vs. IS, and IR vs. IS. The adjusted p-value was calculated following Benjamini and Hochberg [19];"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data have been obtained from the platform Gene Expression Omnibus (GEO) with the identifier “GSE22309” and whose title is “Expression data from human skeletal muscle”. Stimulate files have been used.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38136941", "locator": "sections[10] \"notes\" paragraph 1", "quote": "The data have been obtained from the platform Gene Expression Omnibus (GEO) with the identifier “GSE22309” and whose title is “Expression data from human skeletal muscle”. Stimulate files have been used."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Microarray analysis identified 20 differentially expressed genes for each of three patient-group comparisons, and the resulting gene-expression data supported machine-learning classifiers with accuracy above 80%, reaching 95.42% for three-group classification with the multilayer perceptron.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38136941", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously."}, {"asset": "source:pmid:38136941", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Microarray analysis identified 20 differentially expressed genes for each of three patient-group comparisons, and the resulting gene-expression data supported machine-learning classifiers with accuracy above 80%, reaching 95.42% for three-group classification with the multilayer perceptron.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38136941"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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This study aimed to perform a microarray data analysis to find differentially express", "This work can be broken down into two parts. Microarray data analysis has been one of the most impor", "ics in the last two decades. The analysis of microarray data can be performed in different ways usin", "t tools [10]. In the first part, the classic microarray data analysis is carried out using the Bioco"], "order_basis": "paper_text_order:454", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In this case, the linear models for the microarrays method, implemented in the “limma” package [18], were used to select differentially expressed genes. 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