Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40595011", "kind": "science-record", "revision": 3, "content_hash": "sha256:b4dde1826d609d1ff2cc058b68df1901c0566dec97cbf039edca86619219bef8", "created_at": "2026-11-01T20:30:41Z", "updated_at": "2026-11-01T22:14:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.", "doi": "10.1038/s41598-025-96192-3", "pmid": "40595011", "authors": ["Weibin Wu", "Zheng Peng", "Mingyi Chen", "Yi Yu", "Caisheng Wu", "Qiang Xie"], "journal": "Scientific reports", "year": 2025, "abstract": "Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With the rising prevalence and increasing associated chronic complications, identifying and understanding the molecular mechanisms of diabetes has become an important direction in bioinformatics research. The aim of this study is the identification of diagnostic genes associated with streptozotocin (STZ)-induced diabetic mice. GSE179717 and GSE179718 gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by screening diabetic mice and controls. A total of 45 overlapping genes were recognized as potential DEGs across the two datasets. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the overlapping genes. Protein-protein interaction (PPI) networks were constructed to identify hub genes in the DEGs. After the application of Least absolute shrinkage and selection operator (LASSO), Random Forest analysis, three genes (Scd1, Sirt1 and Hmgb1) were identified as diagnostic genes. Real-time quantitative polymerase chain reaction (RT-qPCR) was supplied to detect the expressions of diagnostic genes in aged diabetic mice. In conclusion, the results of this study suggest that focusing on these genes may provide new targets for the diagnosis and treatment of diabetes.", "claims": [{"id": "claim-method", "statement": "Least absolute shrinkage and selection operator (LASSO) and Random Forest analysis were performed to screen the hub genes using the ‘glmnet’ and ‘randomForest’ package in R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40595011", "locator": "sections[8] \"Identifcation of diagnostic genes\" paragraph 2", "quote": "Least absolute shrinkage and selection operator (LASSO) and Random Forest analysis were performed to screen the hub genes using the ‘glmnet’ and ‘randomForest’ package in R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The dataset analyzed in this study can be derived from public repositories: GSE179717 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE179717), GSE179718 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE179718). Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40595011", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The dataset analyzed in this study can be derived from public repositories: GSE179717 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE179717), GSE179718 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE179718). Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Three diagnostic genes, Scd1, Sirt1 and Hmgb1, were identified and validated in STZ-induced diabetic mice.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40595011", "locator": "sections[17] \"Identifcation and validation of diagnostic genes\" paragraph 2", "quote": "By considering the results from both algorithms, three signature genes were ultimately determined: Scd1, Sirt1 and Hmgb1 (Fig. 8E)."}, {"asset": "source:pmid:40595011", "locator": "sections[17] \"Identifcation and validation of diagnostic genes\" paragraph 2", "quote": "The results indicated that the expression levels of Scd1 and Hmgb1 were upregulated in the STZ-treated group compared to the control group (Fig. 9A-C), while the expression of Sirt1 were downregulated (Fig. 9B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Three diagnostic genes, Scd1, Sirt1 and Hmgb1, were identified and validated in STZ-induced diabetic mice.", "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:40595011"], "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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All mice were housed at 25 ± 2 °C with 55 ± 10% humidity a", "± 10% humidity and a 12-h light/dark cycle. Mice were divided randomly into two groups, the STZ-tr", "the healthy control group. STZ-treated group mice were given a high-fat diet (60.3 kcal%) for 8 wee"], "order_basis": "paper_text_order:2762", "id": "assay:2"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Least absolute shrinkage and selection operator (LASSO) and Random Forest analysis were performed to screen the hub genes using the ‘glmnet’ and ‘randomForest’ package in R software."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}