{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31799347", "kind": "science-record", "revision": 4, "content_hash": "sha256:5584d93a1f0d32ac4c09e71ced093a29b07eaefde4419714d2fa4aac78694ed6", "created_at": "2026-11-01T15:40:52Z", "updated_at": "2026-11-01T21:22:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Expression profiling dataset of competing endogenous RNA in pre-eclampsia.", "doi": "10.1016/j.dib.2019.104795", "pmid": "31799347", "authors": ["Xiaopeng Hu", "Xinyue Li", "Geng G Tian", "Huijuan Zhang", "Weiwei Cheng", "Ji Wu"], "journal": "Data in brief", "year": 2019, "abstract": "This data descriptor is an extended version of the methodologies which have been described in a related paper [1]; its purpose is to disseminate the raw data and analyzed data produced in this experiment. For more insight please see the research article, \"competing endogenous RNA expression profiling in pre-eclampsia identifies hsa_circ_0036877 as a potential novel blood biomarker for early pre-eclampsia\" (Xiaopeng Hu and Junping Ao, 2018) [1]. Using microarray analysis, we investigated competing endogenous RNA (ceRNA) expression profiles in placentas of women with severe pre-eclampsia (SPE) and normal pregnancies. Competing endogenous RNA (ceRNA) refer to RNA transcripts, such as messenger RNA (mRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) that can regulate each other by competing for the same pool of miRNAs. CircRNAs, lncRNAs, and mRNAs differentially expressed between human normal and SPE placentas were obtained in the study. Metadata files were submitted to the Gene Expression Omnibus repository (GEO), with GEO accession number GSE102897. These data are potential useful for further study on the pathogenesis of PE and early prediction of PE onset.", "claims": [{"id": "claim-method", "statement": "The p-value was calculated by a right-sided hypergeometric test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31799347", "locator": "sections[9] \"Microarray analysis\" paragraph 4", "quote": "The p-value was calculated by a right-sided hypergeometric test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data was submitted to the Gene Expression Omnibus repository (GEO) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102897), with GEO accession number GSE102897.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31799347", "locator": "sections[1] \"p\" paragraph 1", "quote": "Data was submitted to the Gene Expression Omnibus repository (GEO) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102897), with GEO accession number GSE102897."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:31799347"], "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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Competing endogenous RNA expression profiling in pre-eclampsia identifies hsa_circ_0036877 as a potential novel blood biomarker for early pre-eclampsia. Clinical epigenetics 2018, 10: 48."]], "row_count": 10, "column_count": 2, "numeric_cells": 4, "header_rows": 0, "locator": "section[1]/table-wrap[1]#undtbl1"}}, {"id": "asset:table:pmid:31799347:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:42c538afec3f76d9d297d8e113926a8632c58dbcebddca0dd1c49bd25322c90a", "bytes": 1014, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[1]/table-wrap[2]#undtbl2", "version": null, "note": null, "structure": {"pmcid": "PMC6881654", "section_index": 1, "section_title": "p", "row_count": 1, "column_count": 1}, "table": {"label": null, "caption": null, "rows": [["Value of the Data•The circRNA, lncRNA and mRNA expression profiling were performed simultaneously, which is generally believed to be one of the major advantages of this research. It is believed that this data is useful for more precise investigation of the interaction between ceRNAs in the process of PE onset.•It could also help PE researchers to resolve the issue of how ceRNAs are involved in the pathogenesis of PE.•The data could be used to analyse predicted targets associated to treatment of PE as it concerns maternal and child health, as well as to identify ceRNAs as early clinical biomarkers of PE.•After bioinformatics analysis, this dataset shows all differentially expressed ceRNAs between human normal and SPE placentas in tables. This dataset also shows all items of functional enrichment and prediction of ceRNAs interaction in tables."]], "row_count": 1, "column_count": 1, "numeric_cells": 0, "header_rows": 0, "locator": "section[1]/table-wrap[2]#undtbl2"}}, {"id": "asset:table:pmid:31799347:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:3f6ea6b4d3927c772ca7d0e69b46340f439eef7b81f0f8514b3db0dcc91f40a8", "bytes": 1057, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[2]/table-wrap[1]#tbl1", "version": null, "note": "Sample description for ceRNAs expression array.", "structure": {"pmcid": "PMC6881654", "section_index": 2, "section_title": "Data", "row_count": 7, "column_count": 8}, "table": {"label": "Table 1", "caption": "Sample description for ceRNAs expression array.", "rows": [["Sample name", "Source", "Organism", "Tissue", "Age", "Protocol 1", "Protocol 2", "Data"], ["SPE_1", "Severe Preeclampsia", "Homo sapiens", "Placenta", "25-35y", "RNA extraction", "circRNA arrayIncRNA and mRNA array", "GSM2747897"], ["SPE_2", "Severe Preeclampsia", "Homo sapiens", "Placenta", "25-35y", "RNA extraction", "circRNA arrayIncRNA and mRNA array", "GSM2747898"], ["SPE_3", "Severe Preeclampsia", "Homo sapiens", "Placenta", "25-35y", "RNA extraction", "circRNA arrayIncRNA and mRNA array", "GSM2747899"], ["N_1", "Normal human", "Homo sapiens", "Placenta", "25-35y", "RNA extraction", "circRNA arrayIncRNA and mRNA array", "GSM2747900"], ["N_2", "Normal human", "Homo sapiens", "Placenta", "25-35y", "RNA extraction", "circRNA arrayIncRNA and mRNA array", "GSM2747901"], ["N_3", "Normal human", "Homo sapiens", "Placenta", "25-35y", "RNA extraction", "circRNA arrayIncRNA and mRNA array", "GSM2747902"]], "row_count": 7, "column_count": 8, "numeric_cells": 20, "header_rows": 1, "locator": "section[2]/table-wrap[1]#tbl1"}}], "gaps": ["asset:data:geo:GSE102897 not downloaded", "asset:data:geo:GSE102897 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Right-sided hypergeometric test", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The p-value was calculated by a right-sided hypergeometric test."}}], "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": "blocked", "tier": "R4", "blocking_axis": "method", "blocked_on": "method:not_determinable", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (Right-sided hypergeometric test), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. 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At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. 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Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. 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