Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38253993", "kind": "science-record", "revision": 3, "content_hash": "sha256:d85a12eba873ce38420b61b7c382483ccc353ac394ec52dac4793641b13a4fc1", "created_at": "2026-11-01T19:20:10Z", "updated_at": "2026-11-01T22:11:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.", "doi": "10.1186/s12859-024-05657-1", "pmid": "38253993", "authors": ["Morteza Rakhshaninejad", "Mohammad Fathian", "Reza Shirkoohi", "Farnaz Barzinpour", "Amir H Gandomi"], "journal": "BMC bioinformatics", "year": 2024, "abstract": "Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.", "claims": [{"id": "claim-method", "statement": "Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38253993", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets examined in this study can be found in the GEO repository, under the NCBI GEO accession numbers GSE42568 [67] and GSE10810 [68]. All codes associated with this paper are available at https://github.com/mornejd/Refining-Breast-Cancer-Biomarker-Discovery-BGWO_SA_Ens. Also, for any additional inquiries contact the corresponding author, Mohammad Fathian (fathian@iust.ac.ir).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38253993", "locator": "sections[38] \"Availability of data and materials\" paragraph 2", "quote": "The datasets examined in this study can be found in the GEO repository, under the NCBI GEO accession numbers GSE42568 [67] and GSE10810 [68]. All codes associated with this paper are available at https://github.com/mornejd/Refining-Breast-Cancer-Biomarker-Discovery-BGWO_SA_Ens. Also, for any additional inquiries contact the corresponding author, Mohammad Fathian (fathian@iust.ac.ir)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "BGWO_SA_Ens identified 1404 genes in the merged dataset and 1710 genes in the GSE45827 dataset, achieving high classification performance.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38253993", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "BGWO_SA_Ens identified 1404 genes in the merged dataset and 1710 genes in the GSE45827 dataset, achieving high classification performance.", "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:38253993"], "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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All codes associated with this paper are available at https://github.com/mornejd/Refining-Breast-Cancer-Biomarker-Discovery-BGWO_SA_Ens. Also, for any additional inquiries contact the corresponding author, Mohammad Fathian (fathian@iust.ac.ir).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets examined in this study can be found in the GEO repository, under the NCBI GEO accession numbers GSE42568 [67] and GSE10810 [68]. All codes associated with this paper are available at https://github.com/mornejd/Refining-Breast-Cancer-Biomarker-Discovery-BGWO_SA_Ens. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:38253993", "data:geo:GSE10810"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE10810"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:05:50Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 20245, "completion_tokens": 1901, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38253993", "type": "paper", "label": "pmid:38253993"}, {"id": "source:pmid:38253993", "type": "manuscript", "label": "PMID 38253993 full text"}, {"id": "data:geo:GSE10810", "type": "dataset", "label": "GEO GSE10810"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38253993", "type": "paper", "label": "pmid:38253993"}, {"id": "source:pmid:38253993", "type": "manuscript", "label": "PMID 38253993 full text"}, {"id": "data:geo:GSE10810", "type": "dataset", "label": "GEO GSE10810"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38253993", "type": "extraction", "used": ["source:pmid:38253993"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE10810", "type": "accession_resolution", "used": ["data:geo:GSE10810"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38253993", "parent_hash": "sha256:35886570a6df3adc1f40df5400e41404b9673404425c617a60b6333e299937f7", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE10810 licence not read", "asset:data:geo:GSE10810 not downloaded", "asset:data:geo:GSE42568 licence not read", "asset:data:geo:GSE42568 not downloaded", "asset:data:geo:GSE45827 licence not read", "asset:data:geo:GSE45827 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Random Forest"], "order_basis": "analysis_only", "id": "assay:1"}]}