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pmid:29379305
A support vector machine and a random forest classifier indicates a 15-miRNA set related to osteosarcoma recurrence.
Yunfei He · Jun Ma · An Wang · Weiheng Wang · Shengchang Luo · Yaoming Liu · Xiaojian Ye
OncoTargets and therapy · 2018 doi:10.2147/OTT.S148394
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Complete file
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It is of great importance to explore the mechanisms of metastasis and recurrence, which are two primary reasons accounting for the high death rate in osteosarcoma.\nDATA AND METHODS: Three miRNA expression profiles related to osteosarcoma were downloaded from GEO DataSets. Differentially expressed miRNAs (DEmiRs) were screened using MetaDE.ES of the MetaDE package. A support vector machine (SVM) classifier was constructed using optimal miRNAs, and its prediction efficiency for recurrence was detected in independent datasets. Finally, a co-expression network was constructed based on the DEmiRs and their target genes.\nRESULTS: In total, 78 significantly DEmiRs were screened. The SVM classifier constructed by 15 miRNAs could accurately classify 58 samples in 65 samples (89.2%) in the GSE39040 database, which was validated in another two databases, GSE39052 (84.62%, 22/26) and GSE79181 (91.3%, 21/23). Cox regression showed that four miRNAs, including hsa-miR-10b, hsa-miR-1227, hsa-miR-146b-3p, and hsa-miR-873, significantly correlated with tumor recurrence time. There were 137, 147, 145, and 77 target genes of the above four miRNAs, respectively, which were assigned to 17 gene ontology functionally annotated terms and 14 Kyoto Encyclopedia of Genes and Genomes pathways. Among them, the \"Osteoclast differentiation\" pathway contained a total of seven target genes and was analyzed further.\nCONCLUSION: The 15-miRNAs-based SVM classifier provides a potential useful tool to predict the recurrence of osteosarcoma. Our results suggest the possible mechanisms of osteosarcoma metastasis and recurrence and provide fresh DEmiRs as potential biomarkers or therapeutic targets for osteosarcoma.", "claims": [{"id": "claim-method", "statement": "With expression values of the optimal feature combination of miRNAs as variables, associations of optimal feature miRNAs with recurrence time were studied by Cox regression analysis using the coxph R function of the survival package in R language.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29379305", "locator": "sections[12] \"Cox regression analysis of optimal feature miRNAs and recurrence time\" paragraph 2", "quote": "With expression values of the optimal feature combination of miRNAs as variables, associations of optimal feature miRNAs with recurrence time were studied by Cox regression analysis using the coxph R function of the survival package in R language."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Three miRNA expression profiles, named GSE39040, GSE39052, and GSE79181, were achieved, among which the former two emerged from the platform “GPL15762 Illumina Human v2 MicroRNA Expression BeadChip”, whereas the last one arose from the platform “GPL15497 Applied Biosystems TaqMan Array Human MicroRNA Cards”. There were 23, 15, and 14 osteosarcoma recurrence samples, and 42, 11, and 9 osteosarcoma non-recurrence samples in these three datasets, separately.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29379305", "locator": "sections[3] \"Collection and pretreatment of expression profile data\" paragraph 2", "quote": "Three miRNA expression profiles, named GSE39040, GSE39052, and GSE79181, were achieved, among which the former two emerged from the platform “GPL15762 Illumina Human v2 MicroRNA Expression BeadChip”, whereas the last one arose from the platform “GPL15497 Applied Biosystems TaqMan Array Human MicroRNA Cards”. There were 23, 15, and 14 osteosarcoma recurrence samples, and 42, 11, and 9 osteosarcoma non-recurrence samples in these three datasets, separately."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using MetaDE.ES to screen differentially expressed miRNAs from three GEO DataSets and an SVM classifier trained on 15 selected miRNAs in GSE39040, the study accurately classified 58 of 65 samples in GSE39040 (89.2%) and achieved 84.62% accuracy in GSE39052 and 91.3% in GSE79181.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:29379305", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Three miRNA expression profiles related to osteosarcoma were downloaded from GEO DataSets. Differentially expressed miRNAs (DEmiRs) were screened using MetaDE.ES of the MetaDE package. A support vector machine (SVM) classifier was constructed using optimal miRNAs, and its prediction efficiency for recurrence was detected in independent datasets."}, {"asset": "source:pmid:29379305", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The SVM classifier constructed by 15 miRNAs could accurately classify 58 samples in 65 samples (89.2%) in the GSE39040 database, which was validated in another two databases, GSE39052 (84.62%, 22/26) and GSE79181 (91.3%, 21/23)."}, {"asset": "source:pmid:29379305", "locator": "sections[22] \"Independent validation and evaluation of SVM classifier performance\" paragraph 2", "quote": "The results showed that the SVM classifier categorized 22 samples precisely in GSE39052, with a classification accuracy rate of 84.62% (22/26); 21 of 23 samples were accurately classified in GSE79181, with 91.3% accuracy rate."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using MetaDE.ES to screen differentially expressed miRNAs from three GEO DataSets and an SVM classifier trained on 15 selected miRNAs in GSE39040, the study accurately classified 58 of 65 samples in GSE39040 (89.2%) and achieved 84.62% accuracy in GSE39052 and 91.3% in GSE79181.", "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:29379305"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE39040"], "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:GSE39040"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:29379305", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/OTT.S148394", "sha256": "sha256:1edee76e8d178e316b79c944e4cda2195dd0a6be9012da1a3af32a5205585f6e", "bytes": 57689, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/OTT.S148394", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC5759858", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE39040", "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=GSE39040", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE39040", "version": null, "note": "Three miRNA expression profiles, named GSE39040, GSE39052, and GSE79181, were achieved, among which the former two emerged from the platform “GPL15762 Illumina Human v2 MicroRNA Expression BeadChip”, whereas the last one arose from the platform “GPL15497 Applied Biosystems TaqMan Array Human MicroRNA Cards”. 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"0.7152", "0"], ["hsa-miR-329", "0.00002", "0.2139", "0.8986", "0"], ["hsa-miR-889", "0.0002", "0.7548", "0.6857", "0"], ["hsa-miR-337-3p", "0.0002", "0.0778", "0.9619", "0"], ["hsa-miR-635", "0.0004", "0.4406", "0.8023", "0"], ["hsa-miR-296-3p", "0.0004", "1.4533", "0.4835", "0"], ["hsa-miR-548c-3p", "0.0006", "0.8417", "0.6565", "0"], ["hsa-miR-625", "0.0006", "0.0724", "0.9644", "0"], ["hsa-miR-551a", "0.0006", "0.2312", "0.8908", "0"], ["hsa-miR-510", "0.0006", "0.0666", "0.9672", "0"], ["hsa-miR-10b", "0.0009", "1.6776", "0.4322", "0"], ["hsa-miR-375", "0.0015", "1.2804", "0.5272", "0"], ["hsa-miR-586", "0.0017", "0.1737", "0.9168", "0"], ["hsa-miR-211", "0.0018", "1.2030", "0.5480", "0"], ["hsa-miR-338-3p", "0.0030", "0.3902", "0.8227", "0"], ["hsa-miR-509-3-5p", "0.0030", "0.5238", "0.7696", "0"], ["hsa-miR-601", "0.0032", "0.8572", "0.6514", "0"], ["hsa-miR-506", "0.0035", "0.1168", "0.9433", "0"], ["hsa-miR-493", "0.0036", "0.8930", "0.6399", "0"], ["hsa-miR-382", "0.0052", "0.4549", "0.7966", "0"], ["hsa-miR-448", "0.0052", "0.3315", "0.8473", "0"], ["hsa-miR-485-3p", "0.0056", "1.9759", "0.3723", "0"], ["hsa-miR-330-3p", "0.0061", "1.7235", "0.4224", "0"], ["hsa-miR-885-5p", "0.0062", "0.4348", "0.8046", "0"], ["hsa-miR-154", "0.0067", "0.5234", "0.7697", "0"], ["hsa-miR-323-3p", "0.0073", "1.4660", "0.4805", "0"], ["hsa-miR-381", "0.0081", "1.0856", "0.5811", "0"], ["hsa-miR-146b-3p", "0.0083", "0.1552", "0.9253", "0"], ["hsa-miR-1324", "0.0092", "0.2516", "0.8818", "0"], ["hsa-miR-544", "0.0095", "0.2217", "0.8951", "0"], ["hsa-miR-342-3p", "0.0099", "1.7962", "0.4073", "0"], ["hsa-miR-944", "0.0122", "0.2137", "0.8987", "0"], ["hsa-miR-409-3p", "0.0124", "0.3380", "0.8445", "0"], ["hsa-miR-362-3p", "0.0128", "1.5226", "0.4671", "0"], ["hsa-miR-520a-3p", "0.0129", "1.0973", "0.5777", "0"], ["hsa-miR-299-5p", "0.0141", "0.9906", "0.6094", "0"], ["hsa-miR-99b", "0.0145", "1.2373", "0.5387", "0"], ["hsa-miR-576-3p", "0.0198", "1.0569", "0.5895", "0"], ["hsa-miR-636", "0.0202", "0.9612", "0.6184", "0"], ["hsa-miR-31", "0.0207", "1.4158", "0.4927", "0"], ["hsa-miR-543", "0.0219", "0.0557", "0.9725", "0"], ["hsa-miR-509-5p", "0.0233", "0.2104", "0.9002", "0"], ["hsa-miR-873", "0.0237", "0.1581", "0.9240", "0"], ["hsa-miR-572", "0.0245", "0.2224", "0.8947", "0"], ["hsa-miR-148b", "0.0251", "1.7011", "0.4272", "0"], ["hsa-miR-324-5p", "0.0262", "1.6948", "0.4285", "0"], ["hsa-miR-422a", "0.0262", "0.3929", "0.8217", "0"], ["hsa-miR-181a", "0.0269", "0.5740", "0.7505", "0"], ["hsa-miR-25", "0.0276", "1.7530", "0.4162", "0"], ["hsa-miR-29c", "0.0286", "0.3219", "0.8513", "0"], ["hsa-miR-517c", "0.0290", "0.7942", "0.6723", "0"], ["hsa-miR-501-5p", "0.0296", "0.7887", "0.6741", "0"], ["hsa-miR-639", "0.0296", "0.6441", "0.7247", "0"], ["hsa-miR-1275", "0.0296", "0.2700", "0.8737", "0"], ["hsa-miR-129-3p", "0.0326", "0.6742", "0.7138", "0"], ["hsa-miR-921", "0.0327", "0.3280", "0.8487", "0"], ["hsa-miR-124", "0.0338", "0.7222", "0.6969", 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"0.7412", "0"], ["hsa-miR-885-3p", "0.0494", "0.8097", "0.6671", "0"], ["hsa-miR-302d", "0.0497", "0.5082", "0.7756", "0"]], "row_count": 79, "column_count": 5, "numeric_cells": 390, "header_rows": 1, "locator": "section[34]/table-wrap[1]#db_ds_table-wrap1_reqid_"}}, {"id": "asset:table:pmid:29379305:8", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:59fededcdd919295607e8d8e146e84c2c974402960b30a55d37a4c1caa716bee", "bytes": 1811, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[34]/table-wrap[2]#db_ds_table-wrap2_reqid_", "version": null, "note": "GO annotation significantly related to target genes", "structure": {"pmcid": "PMC5759858", "section_index": 34, "section_title": "Supplementary Materials", "row_count": 18, "column_count": 4}, "table": {"label": "Table S2", "caption": "GO annotation significantly related to target genes", "rows": [["Term", "Count", "P-value", "Genes"], ["Lymphangiogenesis", "3", "0.0043", "PPP3CB, FLT4, BMPR2"], ["Transmembrane receptor protein serine/threonine kinase activity", "3", "0.0069", "LTBP4, LTBP1, BMPR2"], ["Feeding behavior", "7", "0.0078", "MCHR1, PPYR1, OPRD1, GLS, ASIP, PHF21A, IAPP"], ["RNA polymerase II carboxy-terminal domain kinase activity", "3", "0.0043", "GTF2H1, CDK9, GTF2H4"], ["Copper chaperone activity", "2", "0.0077", "PARK7, CCS"], ["Fatty acid elongation, saturated fatty acid", "2", "0.0077", "ELOVL4, ELOVL6"], ["Fatty acid elongation", "3", "0.0029", "ELOVL2, ELOVL4, ELOVL6"], ["Intracellular receptor signaling pathway", "13", "0.0090", "ZNF536, CCNE1, DCBLD2, PGR, MAP3K7, GPR56, ITCH, NOD1, KANK2, THRA, PARK7, RXRB, DDX5"], ["Negative regulation of protein complex assembly", "7", "0.0071", "THRA, HEY2, SPTAN1, MAP3K7, OPRD1, MAPRE1, PARK7"], ["Positive regulation of stress-activated MAPK cascade", "8", "0.0064", "IL1A, FLT4, MAP3K10, TNFSF11, NOD1, HIPK2, MAP3K7, STK25"], ["Lymph vessel morphogenesis", "3", "0.0059", "PPP3CB, FLT4, BMPR2"], ["Intrinsic apoptotic signaling pathway in response to hydrogen peroxide", "2", "0.0077", "PARK7, STK25"], ["Paracrine signaling", "2", "0.0077", "TNFSF11, PGR"], ["Sertoli cell development", "3", "0.0059", "DMRT1, ARID4B, SDC1"], ["Positive regulation of stress-activated protein kinase signaling cascade", "8", "0.0067", "IL1A, FLT4, MAP3K10, TNFSF11, NOD1, HIPK2, MAP3K7, STK25"], ["Serine/threonine protein kinase complex", "5", "0.0072", "GTF2H1, CDK9, MAP3K7, RB1CC1, GTF2H4"], ["Protein kinase complex", "7", "0.0026", "GTF2H1, CDK9, CCNE1, PRKAB2, GTF2H4, MAP3K7, RB1CC1"]], "row_count": 18, "column_count": 4, "numeric_cells": 40, "header_rows": 1, "locator": "section[34]/table-wrap[2]#db_ds_table-wrap2_reqid_"}}, {"id": "asset:table:pmid:29379305:9", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:aadde1c6a439c75f2573a96c3bacdd032673e288591d4c3a3b6076967aca998a", "bytes": 1308, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[34]/table-wrap[3]#db_ds_table-wrap3_reqid_", "version": null, "note": "The KEGG pathway significantly related to target genes", "structure": {"pmcid": "PMC5759858", "section_index": 34, "section_title": "Supplementary Materials", "row_count": 15, "column_count": 4}, "table": {"label": "Table S3", "caption": "The KEGG pathway significantly related to target genes", "rows": [["Term", "Count", "P-value", "Genes"], ["Arginine biosynthesis", "3", "0.0100", "ARG2, GOT2, GLS"], ["Fatty acid elongation", "3", "0.0171", "ELOVL2, ELOVL4, ELOVL6"], ["Nucleotide excision repair", "4", "0.0178", "GTF2H1, LIG1, RAD23B, GTF2H4"], ["Osteoclast differentiation", "7", "0.0195", "IL1A, CAMK4, TNFSF11, NCF4, PPP3CB, MAP3K7, SOCS3"], ["Rheumatoid arthritis", "5", "0.0379", "ITGAL, TNFSF11, IL8, MMP3, 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downloaded", "asset:data:geo:GSE39052 licence not read", "asset:data:geo:GSE79181 not downloaded", "asset:data:geo:GSE79181 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "With expression values of the optimal feature combination of miRNAs as variables, associations of optimal feature miRNAs with recurrence time were studied by Cox regression analysis using the coxph R function of the survival package in R language."}}, {"id": "method-2", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-3", "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": "support vector machine"}}], "targets": [], "gaps": []}, "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": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "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": ["GSE39040", "GSE39052", "GSE79181"], "resolved_accessions": ["GSE39040", "GSE39052", "GSE79181"], "statements": ["Three miRNA expression profiles, named GSE39040, GSE39052, and GSE79181, were achieved, among which the former two emerged from the platform “GPL15762 Illumina Human v2 MicroRNA Expression BeadChip”, whereas the last one arose from the platform “GPL15497 Applied Biosystems TaqMan Array Human MicroRNA Cards”. There were 23, 15, and 14 osteosarcoma recurrence samples, and 42, 11, and 9 osteosarcoma non-recurrence samples in these three datasets, separately."], "basis": "The paper names GSE39040, GSE39052, GSE79181 in GEO DataSets and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:29379305", "data:geo:GSE39040", "asset:table:pmid:29379305:1", "asset:table:pmid:29379305:2", "asset:table:pmid:29379305:3", "asset:table:pmid:29379305:4", "asset:table:pmid:29379305:5", "asset:table:pmid:29379305:6", "asset:table:pmid:29379305:7", "asset:table:pmid:29379305:8", "asset:table:pmid:29379305:9"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE39040"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:33:29Z", "independence": "same_provider"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 8, "usage": {"prompt_tokens": 140010, "completion_tokens": 6107, "cached_tokens": 2113}}, "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:29379305", "type": "paper", "label": "pmid:29379305"}, {"id": "source:pmid:29379305", "type": "manuscript", "label": "PMID 29379305 full text"}, {"id": "data:geo:GSE39040", "type": "dataset", "label": "GEO GSE39040"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:29379305", "type": "paper", "label": "pmid:29379305"}, {"id": "source:pmid:29379305", "type": "manuscript", "label": "PMID 29379305 full text"}, {"id": "data:geo:GSE39040", "type": "dataset", "label": "GEO GSE39040"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-29379305", "type": "extraction", "used": ["source:pmid:29379305"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE39040", "type": "accession_resolution", "used": ["data:geo:GSE39040"], "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:29379305", "parent_hash": "sha256:813dfbcaaf6cd7d4dfc7dfb0f2c347a05e7743a1f642a3256b68261652ff4512", "fork_of": null, "generation": 7}, "gaps": ["asset:data:geo:GSE39040 licence not read", "asset:data:geo:GSE39040 not downloaded", "asset:data:geo:GSE39052 licence not read", "asset:data:geo:GSE39052 not downloaded", "asset:data:geo:GSE79181 licence not read", "asset:data:geo:GSE79181 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["With expression values of the optimal feature combination of miRNAs as variables, associations of optimal feature miRNAs with recurrence time were studied by Cox regression analysis using the coxph R function of the survival package in R language."], "order_basis": "analysis_only", "id": "assay:1"}]}