Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36276977", "kind": "science-record", "revision": 5, "content_hash": "sha256:6454aa132c43ad7333ed0c474fb180b27bd7fcaecbde2c896323d07d30e774bd", "created_at": "2026-11-01T18:15:41Z", "updated_at": "2026-11-01T22:50:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest.", "doi": "10.3389/fgene.2022.957718", "pmid": "36276977", "authors": ["Shoufei Wang", "Wenfei Liu", "Ziheng Ye", "Xiaotian Xia", "Minggao Guo"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Objective: Papillary thyroid carcinoma (PTC) accounts for 80% of thyroid malignancy, and the occurrence of PTC is increasing rapidly. The present study was conducted with the purpose of identifying novel and important gene panels and developing an early diagnostic model for PTC by combining artificial neural network (ANN) and random forest (RF). Methods and results: Samples were searched from the Gene Expression Omnibus (GEO) database, and gene expression datasets (GSE27155, GSE60542, and GSE33630) were collected and processed. GSE27155 and GSE60542 were merged into the training set, and GSE33630 was defined as the validation set. Differentially expressed genes (DEGs) in the training set were obtained by \"limma\" of R software. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as immune cell infiltration analysis were conducted based on DEGs. Important genes were identified from the DEGs by random forest. Finally, an artificial neural network was used to develop a diagnostic model. Also, the diagnostic model was validated by the validation set, and the area under the receiver operating characteristic curve (AUC) value was satisfactory. Conclusion: A diagnostic model was established by a joint of random forest and artificial neural network based on a novel gene panel. The AUC showed that the diagnostic model had significantly excellent performance.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) were identified in 34 normal samples and 84 PTC samples of the training set through the R package “limma” using the classic Bayesian data analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36276977", "locator": "sections[5] \"Screening for differentially expressed genes\" paragraph 2", "quote": "Differentially expressed genes (DEGs) were identified in 34 normal samples and 84 PTC samples of the training set through the R package “limma” using the classic Bayesian data analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36276977", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE27155"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE27155"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36276977", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.957718", "sha256": "sha256:50e1f48d94c4bacd4eea184831884fbd36a77fe72beb0daa4236aad3cad39b7c", "bytes": 31268, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.957718", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9585230", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE27155", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27155", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE27155", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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