Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31001458", "kind": "science-record", "revision": 8, "content_hash": "sha256:7bea7375da040a6be54a9f037f5c20b470e9586f896940b2e1224f80cb14429a", "created_at": "2026-11-01T15:40:44Z", "updated_at": "2026-11-01T22:00:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.", "doi": "10.7759/cureus.4004", "pmid": "31001458", "authors": ["Fateme Shaabanpour Aghamaleki", "Behrouz Mollashahi", "Mokhtar Nosrati", "Afshin Moradi", "Mojgan Sheikhpour", "Abolfazl Movafagh"], "journal": "Cureus", "year": 2019, "abstract": "Introduction Chronic lymphocytic leukemia (CLL) is one of the most common types of leukemia, and the early diagnosis of patients coincides with their proper treatment and survival. If patients are diagnosed late or proper treatment is not applied, it may lead to harmful results. Several methods could be used for the diagnosis of leukemia; some of these include complete blood count (CBC), immunophenotyping, lymph node biopsy, chest X-ray, computerized tomography (CT) scan, and ultrasound. Most of these methods are time-consuming and an application of more than one method will result as intended. This acknowledgment stresses the necessity of rapid and proper diagnosis for leukemia based on clinical and medical findings, inasmuch as it was decided to apply the artificial neural network (ANN) in order to identify a molecular biomarker for rapid leukemia diagnosis from blood samples and evaluate its potential for the detection of cancer. Materials & methods The independent sample t-test was applied with the Statistical Package for the Social Sciences (SPSS; IBM Corp, Armonk, NY, US) software on the microarray gene expression data of Gene Expression Omnibus (GEO) datasets (GSE22529); 12 genes that had shown the highest differences (among parameters whose p-value was less than 0.01) were selected for further ANN analysis. The selected genes of 53 patients were applied to the training network algorithm, with a learning rate of 0.1. Results The results showed a high accuracy of the relationship between the output of the trained network and the test data. The area under the receiver operating characteristic (ROC) curve was 0.991, which provides proof of the precision and the relationship with identifying Gelsolin as a potential biomarker for this research. Conclusions With these results, it was concluded that the training process of the ANN could be applied to rapid CLL diagnosis and finding a potential biomarker. Besides, it is suggested that this method could be performed to diagnose other forms of cancer in order to get a rapid and reliable outcome.", "claims": [{"id": "claim-method", "statement": "Then, after a primary statistical analysis, the two-tailed student t-test was used to determine the statistical significance for the difference between the two groups of CLL patients and healthy individuals.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31001458", "locator": "sections[2] \"Materials and methods\" paragraph 5", "quote": "Then, after a primary statistical analysis, the two-tailed student t-test was used to determine the statistical significance for the difference between the two groups of CLL patients and healthy individuals."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression profile was generated from peripheral blood samples (n=104). 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These twelve genes were selected based on their lowest p-value. The probe ID of the genes in the microarray and their names and gene symbols were identified.", "structure": {"pmcid": "PMC6450593", "section_index": 3, "section_title": "Results", "row_count": 13, "column_count": 4}, "table": {"label": "Table 1", "caption": "A list of the twelve selected genes based on the t-test result. These twelve genes were selected based on their lowest p-value. The probe ID of the genes in the microarray and their names and gene symbols were identified.", "rows": [["Probe ID", "Gene symbol", "Species", "Gene name"], ["200666_s_at", "DNAJB1", "Homo sapiens", "DnaJ heat shock protein family (Hsp40) member B1"], ["200627_at", "PTGES3", "Homo sapiens", "Prostaglandin E Synthase 3"], ["200664_s_at", "DNAJB1", "Homo sapiens", "DnaJ heat shock protein family (Hsp40) member B1"], ["200701_at", "NPC2", "Homo sapiens", "NPC intracellular cholesterol transporter 2"], ["200675_at", "CD81", "Homo sapiens", "CD81 molecule"], ["200028_s_at", "STARD7", "Homo sapiens", "StAR related lipid transfer domain 7"], ["200634_at", "PFN1", "Homo sapiens", "Profilin 1"], ["200709_at", "FKBP1A", "Homo sapiens", "FK506 binding protein 1A"], ["200022_at", "RPL18", "Homo sapiens", "Ribosomal Protein L18"], ["200696_s_at", "GSN", "Homo sapiens", "Gelsolin"], ["200657_at", "SLC25A5", "Homo sapiens", "Solute Carrier Family 25 Member 5"], ["200650_s_at", "LDHA", "Homo sapiens", "Lactate Dehydrogenase A"]], "row_count": 13, "column_count": 4, "numeric_cells": 25, "header_rows": 0, "locator": "section[3]/table-wrap[1]#TAB1"}}, {"id": "asset:table:pmid:31001458:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:78229355ff3078002b4a7631a80e234d019bd70a7c0914ed4e39bfb8215f692c", "bytes": 555, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[3]/table-wrap[2]#TAB2", "version": null, "note": "The results of three algorithms for the twelve genes in two groups of patients and healthy.AUC = Area under curve in the ROC analysis, CA = Classification accuracy, F1 = An index of reliability", "structure": {"pmcid": "PMC6450593", "section_index": 3, "section_title": "Results", "row_count": 4, "column_count": 6}, "table": {"label": "Table 2", "caption": "The results of three algorithms for the twelve genes in two groups of patients and healthy.AUC = Area under curve in the ROC analysis, CA = Classification accuracy, F1 = An index of reliability", "rows": [["Method", "AUC", "CA", "F1", "Precision", "Recall"], ["SVM", "0.985", "0.952", "0.953", "0.955", "0.952"], ["Random Forest", "0.969", "0.936", "0.936", "0.936", "0.936"], ["Neural network", "0.991", "0.969", "0.969", "0.970", "0.969"]], "row_count": 4, "column_count": 6, "numeric_cells": 15, "header_rows": 0, "locator": "section[3]/table-wrap[2]#TAB2"}}, {"id": "asset:table:pmid:31001458:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:7b3ff378e269168d7ee82f9fbd7e850f6f96e66ebdf1fdd4a38fbe0841e0c10b", "bytes": 663, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[3]/table-wrap[3]#TAB3", "version": null, "note": "The results of the neural network algorithm for the twelve genes between the two groups.The results of the neural network algorithm for the 12 genes between the two groups. This algorithm was applied for the 12 selected genes, to understand the ANN value in classification.ANN = Artificial neural network, AUC = Area under curve in the ROC analysis, CA = Classification accuracy, F1 = An index of reliability", "structure": {"pmcid": "PMC6450593", "section_index": 3, "section_title": "Results", "row_count": 2, "column_count": 6}, "table": {"label": "Table 3", "caption": "The results of the neural network algorithm for the twelve genes between the two groups.The results of the neural network algorithm for the 12 genes between the two groups. This algorithm was applied for the 12 selected genes, to understand the ANN value in classification.ANN = Artificial neural network, AUC = Area under curve in the ROC analysis, CA = Classification accuracy, F1 = An index of reliability", "rows": [["Method", "AUC", "CA", "F1", "Precision", "Recall"], ["Neural network", "0.991", "0.981", "0.980", "0.981", "0.981"]], "row_count": 2, "column_count": 6, "numeric_cells": 5, "header_rows": 0, "locator": "section[3]/table-wrap[3]#TAB3"}}, {"id": "asset:table:pmid:31001458:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:442b3856deff4effda0c3d28c39a999f85029864398daf415b9dd8a4b0ae12a7", "bytes": 1332, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[3]/table-wrap[4]#TAB4", "version": null, "note": "Results of receiver operating characteristic curve analysis of the twelve genes.Results of the ROC curve analysis of the 12 genes were indicated and according to this result, GSN has higher AUC; therefore, it can diagnose CLL samples as compared to healthy samples.AUC = Area under the curve in the ROC analysis, ROC = receiver operating characteristic, GSN = Gelsolin, CLL = Chronic lymphocytic leukemia", "structure": {"pmcid": "PMC6450593", "section_index": 3, "section_title": "Results", "row_count": 13, "column_count": 5}, "table": {"label": "Table 4", "caption": "Results of receiver operating characteristic curve analysis of the twelve genes.Results of the ROC curve analysis of the 12 genes were indicated and according to this result, GSN has higher AUC; therefore, it can diagnose CLL samples as compared to healthy samples.AUC = Area under the curve in the ROC analysis, ROC = receiver operating characteristic, GSN = Gelsolin, CLL = Chronic lymphocytic leukemia", "rows": [["Probe ID", "Gene symbol", "Specificity", "Sensitivity", "AUC"], ["200022_at", "RPL18", "0.756097561", "1", "0.911308204"], ["200028_s_at", "STARD7", "0.818181818", "0.926829268", "0.88691796"], ["200627_at", "PTGES3", "0.727272727", "0.951219512", "0.840354767"], ["200650_s_at", "LDHA", "0.731707317", "1", "0.922394678"], ["200657_at", "SLC25A5", "0.804878049", "1", "0.953436807"], ["200664_s_at", "DNAJB1", "0.902439024", "0.909090909", "0.931263858"], ["200666_s_at", "DNAJB1", "0.853658537", "1", "0.922394678"], ["200675_at", "CD81", "0.951219512", "0.909090909", "0.911308204"], ["200701_at", "NPC2", "0.902439024", "0.909090909", "0.940133038"], ["200709_at", "FKBP1A", "0.829268293", "1", "0.968957871"], ["200696_s_at", "GSN", "0.902439024", "1", "0.971175166"], ["200634_at", "PFN1", "0.926829268", "0.818181818", "0.89578714"]], "row_count": 13, "column_count": 5, "numeric_cells": 51, "header_rows": 0, "locator": "section[3]/table-wrap[4]#TAB4"}}], "gaps": ["asset:data:geo:GSE22529 not downloaded", "asset:data:geo:GSE22529 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "student_t_test", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["student_t"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Then, after a primary statistical analysis, the two-tailed student t-test was used to determine the statistical significance for the difference between the two groups of CLL patients and healthy individuals."}}, {"id": "method-2", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Support Vector Machine"}}, {"id": "method-3", "family": "machine_learning", "specific": "decision_tree", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Decision Tree"}}, {"id": "method-4", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "AUROC"}}], "targets": [], "gaps": ["method.student_t"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["student_t"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: student_t_test."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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