Identifying effective diagnostic biomarkers for childhood cerebral malaria in Africa integrating coexpression analysis with machine learning algorithm.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36782344", "kind": "science-record", "revision": 4, "content_hash": "sha256:cf36fc09c22244525e476ea23b15248e75032a2abcd7df3e86a41d205da74015", "created_at": "2026-11-01T19:01:22Z", "updated_at": "2026-11-01T22:51:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying effective diagnostic biomarkers for childhood cerebral malaria in Africa integrating coexpression analysis with machine learning algorithm.", "doi": "10.1186/s40001-022-00980-w", "pmid": "36782344", "authors": ["Jia-Xin Li", "Wan-Zhe Liao", "Ze-Min Huang", "Xin Yin", "Shi Ouyang", "Bing Gu", "Xu-Guang Guo"], "journal": "European journal of medical research", "year": 2023, "abstract": "BACKGROUND: Cerebral malaria (CM) is a manifestation of malaria caused by plasmodium infection. It has a high mortality rate and severe neurological sequelae, existing a significant research gap and requiring further study at the molecular level.\nMETHODS: We downloaded the GSE117613 dataset from the Gene Expression Omnibus (GEO) database to determine the differentially expressed genes (DEGs) between the CM group and the control group. Weighted gene coexpression network analysis (WGCNA) was applied to select the module and hub genes most relevant to CM. The common genes of the key module and DEGs were selected to perform further analysis. The least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) were applied to screen and verify the diagnostic markers of CM. Eventually, the hub genes were validated in the external dataset. Gene set enrichment analysis (GSEA) was applied to investigate the possible roles of the hub genes.\nRESULTS: The GO and KEGG results showed that DEGs were enriched in some neutrophil-mediated pathways and associated with some lumen structures. Combining LASSO and the SVM-RFE algorithms, LEF1 and IRAK3 were identified as potential hub genes in CM. Through the GSEA enrichment results, we found that LEF1 and IRAK3 participated in maintaining the integrity of the blood-brain barrier (BBB), which contributed to improving the prognosis of CM.\nCONCLUSIONS: This study may help illustrate the pathophysiology of CM at the molecular level. LEF1 and IRAK3 can be used as diagnostic biomarkers, providing new insight into the diagnosis and prognosis prediction in pediatric CM.", "claims": [{"id": "claim-method", "statement": "Subsequently, the limma package was also applied to identify the DEGs between the CM and control groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36782344", "locator": "sections[3] \"Differentially expressed genes (DEGs) screening and functional enrichment analysis\" paragraph 2", "quote": "Subsequently, the limma package was also applied to identify the DEGs between the CM and control groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in our study are available in the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36782344", "locator": "sections[18] \"notes\" paragraph 1", "quote": "The datasets analyzed in our study are available in the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:36782344"], "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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