Construction and analysis of a conjunctive diagnostic model of HNSCC with random forest and artificial neural network.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37185487", "kind": "science-record", "revision": 3, "content_hash": "sha256:708cc3db2d944374151c0b0be04a8af1a96212b09e6f106e3ba07981c9039187", "created_at": "2026-11-01T19:01:52Z", "updated_at": "2026-11-01T22:09:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and analysis of a conjunctive diagnostic model of HNSCC with random forest and artificial neural network.", "doi": "10.1038/s41598-023-32620-6", "pmid": "37185487", "authors": ["Yao Luo", "Liu-Qing Zhou", "Fan Yang", "Jing-Cai Chen", "Jian-Jun Chen", "Yan-Jun Wang"], "journal": "Scientific reports", "year": 2023, "abstract": "Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor that is highly aggressive and ranks fifth among the most common cancers worldwide. Although, the researches that attempted to construct a diagnostic model were deficient in HNSCC. Currently, the gold standard for diagnosing head and neck tumors is pathology, but this requires a traumatic biopsy. There is still a lack of a noninvasive test for such a high-incidence tumor. In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized. The data of HNSCC gene expression was accessed from Gene Expression Omnibus (GEO) database; we selected three datasets totally, and we combined 2 datasets (GSE6631 and GSE55547) for screening differentially expressed genes (DEGs) and chose another dataset (GSE13399) for validation. Firstly, the 6 DEGs (CRISP3, SPINK5, KRT4, MMP1, MAL, SPP1) were screened by RF. Subsequently, ANN was applied to calculate the weights of 6 genes. Besides, we created a diagnostic model and nominated it as neuralHNSCC, and the performance of neuralHNSCC by area under curve (AUC) was verified using another dataset. Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort. Furthermore, we used the Cell-type Identification using Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to investigate the difference in immune cell infiltration between HNSCC and normal tissues initially. The selected 6 DEGs and the constructed novel diagnostic model of HNSCC would make contributions to the diagnosis.", "claims": [{"id": "claim-method", "statement": "In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37185487", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37185487", "locator": "sections[20] \"Data availability\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The neuralHNSCC diagnostic model achieved an AUC of 0.998 in the training cohort and 0.734 in the validation cohort.", "kind": "quantitative", "target": {"value": 0.998, "unit": "auc", "tolerance": null, "source": "Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort."}, "evidence": [{"asset": "source:pmid:37185487", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The neuralHNSCC diagnostic model achieved an AUC of 0.998 in the training cohort and 0.734 in the validation cohort.", "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:37185487"], "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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