Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39587559", "kind": "science-record", "revision": 4, "content_hash": "sha256:3911cc438ae4e82786dd2ce6ededaca7be6dd56ea4259e201a095ed7f44031c4", "created_at": "2026-11-01T20:29:51Z", "updated_at": "2026-11-01T22:55:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms.", "doi": "10.1186/s13036-024-00466-9", "pmid": "39587559", "authors": ["Shanping Shi", "Chao Huang", "Xiaojian Tang", "Hua Liu", "Weiwei Feng", "Chen Chen"], "journal": "Journal of biological engineering", "year": 2024, "abstract": "This study addresses the challenges in the early diagnosis of deep infiltrating endometriosis (DIE) by exploring the potential role of the deubiquitinating enzyme USP14. By analyzing the GSE141549 dataset from the Gene Expression Omnibus (GEO) database, using bioinformatics methods and three machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), the key feature gene USP14 was identified. The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786). Further analysis revealed the important role of USP14 in muscle function, cellular growth factor response, and maintenance of chromosome structure, and its close association with various immune cell functions. Immunohistochemical staining confirmed the high expression of USP14 in DIE tissues. This study provides a new molecular target for the early diagnosis of DIE, which holds significant clinical implications and potential application value.", "claims": [{"id": "claim-method", "statement": "Three different machine learning methods, the LASSO, Random Forest (RF), and SVM, were employed for decision-making and variable selection.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39587559", "locator": "sections[15] \"Identification of USP14\" paragraph 2", "quote": "Three different machine learning methods, the LASSO, Random Forest (RF), and SVM, were employed for decision-making and variable selection."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No datasets were generated or analysed during the current study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39587559", "locator": "sections[29] \"notes\" paragraph 1", "quote": "No datasets were generated or analysed during the current study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786).", "kind": "quantitative", "target": {"value": 0.786, "unit": "auc", "tolerance": null, "source": "The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786)."}, "evidence": [{"asset": "source:pmid:39587559", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786).", "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:39587559"], "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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