Cancer classification: Mutual information, target network and strategies of therapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:23031749", "kind": "science-record", "revision": 8, "content_hash": "sha256:67b0d56230521bcc070349e1daa0fcd1d2aa745b0f7fa5d635276a22cdb08850", "created_at": "2026-11-01T15:40:02Z", "updated_at": "2026-11-01T21:58:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Cancer classification: Mutual information, target network and strategies of therapy.", "doi": "10.1186/2043-9113-2-16", "pmid": "23031749", "authors": ["Wen-Chin Hsu", "Chan-Cheng Liu", "Fu Chang", "Su-Shing Chen"], "journal": "Journal of clinical bioinformatics", "year": 2012, "abstract": "BACKGROUND: Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy.\nMETHODS: We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms.\nRESULTS: These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative.\nCONCLUSIONS: We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves the traditional single target/single component analysis methods. This framework builds a computational foundation which can provide a clear classification of cancers and lead to an efficient cancer therapy.", "claims": [{"id": "claim-method", "statement": "We then apply the MI calculation described in the Mutual Information section on 96 biomarkers for GSE18655 and represent the pairwise MI values of grade 1, grade 2 and grade 3 samples in three 96*96 matrixes which can be found in Additional file 5 GSE18655 Grade1 MI.xlsx, Additional file 6 GSE18655 Grade2 MI.xlsx and Additional file 7 GSE18655 Grade3 MI.xlsx.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:23031749", "locator": "sections[17] \"Results of AMFES\" paragraph 4", "quote": "We then apply the MI calculation described in the Mutual Information section on 96 biomarkers for GSE18655 and represent the pairwise MI values of grade 1, grade 2 and grade 3 samples in three 96*96 matrixes which can be found in Additional file 5 GSE18655 Grade1 MI.xlsx, Additional file 6 GSE18655 Grade2 MI.xlsx and Additional file 7 GSE18655 Grade3 MI.xlsx."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The prostate cancer dataset, GSE18655, includes 139 patients with 502 molecular markers, RNAs [21]. Among these patients, 38 patient samples have GS 5–6 corresponding to histologic grade 1, 90 samples have GS 7 corresponding to histologic grade 2 and 11 samples have GS 8–9 corresponding to histologic grade 3.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:23031749", "locator": "sections[14] \"The prostate cancer dataset with RNA biomarkers\" paragraph 3", "quote": "The prostate cancer dataset, GSE18655, includes 139 patients with 502 molecular markers, RNAs [21]."}, {"asset": "source:pmid:23031749", "locator": "sections[14] \"The prostate cancer dataset with RNA biomarkers\" paragraph 3", "quote": "Among these patients, 38 patient samples have GS 5–6 corresponding to histologic grade 1, 90 samples have GS 7 corresponding to histologic grade 2 and 11 samples have GS 8–9 corresponding to histologic grade 3."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper used AMFES-based SVM classification with mutual-information analysis on three GEO datasets—GSE18655, GSE19536, and GSE21036—and selected 96 biomarkers for three-stage prostate cancer, 72 for luminal-versus-basal/normal breast cancer, 68 for basal-like-versus-normal-like breast cancer, and 22 for prostate cancerous-versus-normal classification.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:23031749", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification."}, {"asset": "source:pmid:23031749", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in."}, {"asset": "source:pmid:23031749", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal."}, {"asset": "source:pmid:23031749", "locator": "sections[18] \"Results of calculating mutual information\" paragraph 2", "quote": "The statistic results of calculating mutual information are shown in Table 3 at the end of this paper."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper used AMFES-based SVM classification with mutual-information analysis on three GEO datasets—GSE18655, GSE19536, and GSE21036—and selected 96 biomarkers for three-stage prostate cancer, 72 for luminal-versus-basal/normal breast cancer, 68 for basal-like-versus-normal-like breast cancer, and 22 for prostate cancerous-versus-normal classification.", "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:23031749"], "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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"section[17]/table-wrap[1]#T2"}}, {"id": "asset:table:pmid:23031749:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:4e0f87c7c529ee475a3d9f6a1dc6ad57a05855d145d30d19b06a8237e607887a", "bytes": 1041, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[2]#T3", "version": null, "note": "Results of analysis ofMI matrices", "structure": {"pmcid": "PMC3524788", "section_index": 17, "section_title": "Results of AMFES", "row_count": 10, "column_count": 8}, "table": {"label": "Table 3", "caption": "Results of analysis ofMI matrices", "rows": [["", "Mean value of MI", "Standard deviation of MI", "Num of positive values", "Num of negative values", "Num of values ofdifferent sign", "Min value", "Max value"], ["GSE18655_grade1", "0.00024", "0.0015", "6298", "2918", "N/A", "−0.0011", "0.0858"], ["GSE18655_grade2", "0.00020", "0.0017", "6468", "2748", "", "−0.0018", "0.0949"], ["GSE18655_grade3", "0.0004", "0.0021", "6650", "2566", "", "−0.0029", "0.0582"], ["GSE19536_A(72)", "0.00036", "0.0022", "3912", "1272", "2052", "−0.0010", "0.1293"], ["GSE19536_B(72)", "0.00053", "0.0040", "3388", "1796", "", "−0.0022", "0.2279"], ["GSE19536_BasalLike(68)", "0.0017", "0.0056", "3491", "998", "1217", "−0.0033", "0.1648"], ["GSE19536_NormalLike(68)", "0.0056", "0.008", "4200", "420", "", "−0.002", "0.1279"], ["GSE21036_cancer", "0.0165", "0.0212", "10", "474", "56", "−0.002", "0.1446"], ["GSE21036_norm", "0.0086", "0.0146", "46", "438", "", "−0.0015", "0.1565"]], "row_count": 10, "column_count": 8, "numeric_cells": 66, "header_rows": 1, "locator": "section[17]/table-wrap[2]#T3"}}], "gaps": ["asset:data:geo:GSE18655 not downloaded", "asset:data:geo:GSE18655 licence not read", "asset:data:geo:GSE19536 not downloaded", "asset:data:geo:GSE19536 licence not read", "asset:data:geo:GSE21036 not downloaded", "asset:data:geo:GSE21036 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 0.7, "band": "medium", "quote": "SVM"}}, {"id": "method-2", "family": null, "specific": null, "label": "Mutual information (MI)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We then apply the MI calculation described in the Mutual Information section on 96 biomarkers for GSE18655 and represent the pairwise MI values of grade 1, grade 2 and grade 3 samples in three 96*96 matrixes which can be found in Additional file 5 GSE18655 Grade1 MI.xlsx, Additional file 6 GSE18655 Grade2 MI.xlsx and Additional file 7 GSE18655 Grade3 MI.xlsx."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "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": true, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: support_vector_machine."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE18655", "GSE19536", "GSE21036"], "resolved_accessions": ["GSE18655", "GSE19536", "GSE21036"], "statements": ["The prostate cancer dataset, GSE18655, includes 139 patients with 502 molecular markers, RNAs [21]. Among these patients, 38 patient samples have GS 5–6 corresponding to histologic grade 1, 90 samples have GS 7 corresponding to histologic grade 2 and 11 samples have GS 8–9 corresponding to histologic grade 3."], "basis": "The paper names GSE18655, GSE19536, GSE21036 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:23031749", "data:geo:GSE18655", "asset:table:pmid:23031749:1", "asset:table:pmid:23031749:2", "asset:table:pmid:23031749:3"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE18655"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:21:09Z", "independence": "same_provider"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 8, "usage": {"prompt_tokens": 81102, "completion_tokens": 3967, "cached_tokens": 61412}}, "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:23031749", "type": "paper", "label": "pmid:23031749"}, {"id": "source:pmid:23031749", "type": "manuscript", "label": "PMID 23031749 full text"}, {"id": "data:geo:GSE18655", "type": "dataset", "label": "GEO GSE18655"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:23031749", "type": "paper", "label": "pmid:23031749"}, {"id": "source:pmid:23031749", "type": "manuscript", "label": "PMID 23031749 full text"}, {"id": "data:geo:GSE18655", "type": "dataset", "label": "GEO GSE18655"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-23031749", "type": "extraction", "used": ["source:pmid:23031749"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE18655", "type": "accession_resolution", "used": ["data:geo:GSE18655"], "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:23031749", "parent_hash": "sha256:0bf9ec907dfe083b2de3949fbf5f3e8785c9e01e0e250fc5dc94b5875c84216d", "fork_of": null, "generation": 7}, "gaps": ["asset:data:geo:GSE18655 licence not read", "asset:data:geo:GSE18655 not downloaded", "asset:data:geo:GSE19536 licence not read", "asset:data:geo:GSE19536 not downloaded", "asset:data:geo:GSE21036 licence not read", "asset:data:geo:GSE21036 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["we only use the GSE 21036 for analysis. The microarray dataset has 142 samples which include 114 pr", ". The platform is Agilent-019118 Human miRNA Microarray 2.0 G4470B (miRNA ID version). Supplementary"], "order_basis": "paper_text_order:2791", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["SVM"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}