CorrelaGenes: a new tool for the interpretation of the human transcriptome.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:24564370", "kind": "science-record", "revision": 6, "content_hash": "sha256:c38f6bb83f7e5562bae7ba61b1679e21693c9ccb83d8503572a107d24b319456", "created_at": "2026-11-01T15:40:04Z", "updated_at": "2026-11-01T21:19:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "CorrelaGenes: a new tool for the interpretation of the human transcriptome.", "doi": "10.1186/1471-2105-15-S1-S6", "pmid": "24564370", "authors": ["Paolo Cremaschi", "Sergio Rovida", "Lucia Sacchi", "Antonella Lisa", "Francesca Calvi", "Alessandra Montecucco", "Giuseppe Biamonti", "Silvia Bione", "Gianni Sacchi"], "journal": "BMC bioinformatics", "year": 2014, "abstract": "BACKGROUND: The amount of gene expression data available in public repositories has grown exponentially in the last years, now requiring new data mining tools to transform them in information easily accessible to biologists.\nRESULTS: By exploiting expression data publicly available in the Gene Expression Omnibus (GEO) database, we developed a new bioinformatics tool aimed at the identification of genes whose expression appeared simultaneously altered in different experimental conditions, thus suggesting co-regulation or coordinated action in the same biological process. To accomplish this task, we used the 978 human GEO Curated DataSets and we manually performed the selection of 2,109 pair-wise comparisons based on their biological rationale. The lists of differentially expressed genes, obtained from the selected comparisons, were stored in a PostgreSQL database and used as data source for the CorrelaGenes tool. Our application uses a customized Association Rule Mining (ARM) algorithm to identify sets of genes showing expression profiles correlated with a gene of interest. The significance of the correlation is measured coupling the Lift, a well-known standard ARM index, and the χ(2) p value. The manually curated selection of the comparisons and the developed algorithm constitute a new approach in the field of gene expression profiling studies. Simulation performed on 100 randomly selected target genes allowed us to evaluate the efficiency of the procedure and to obtain preliminary data demonstrating the consistency of the results.\nCONCLUSIONS: The preliminary results of the simulation showed how CorrelaGenes could contribute to the characterization of molecular pathways and biological processes integrating data obtained from other applications and available in public repositories.", "claims": [{"id": "claim-method", "statement": "We perform a χ2 test to evaluate the number of times the target gene and gene X result simultaneously differentially expressed compared to the expected value in the target population.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:24564370", "locator": "sections[4] \"ARM algorithm\" paragraph 16", "quote": "We perform a χ2 test to evaluate the number of times the target gene and gene X result simultaneously differentially expressed compared to the expected value in the target population."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The source of the expression data used by the CorrelaGenes tool was built from 978 human GEO Curated DataSets (GDS) downloaded from the GEO archive through the GEOquery 2.21.9 R/Bioconductor package [10].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:24564370", "locator": "sections[3] \"Data pre-processing\" paragraph 2", "quote": "The source of the expression data used by the CorrelaGenes tool was built from 978 human GEO Curated DataSets (GDS) downloaded from the GEO archive through the GEOquery 2.21.9 R/Bioconductor package [10]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CorrelaGenes applies a customized Association Rule Mining algorithm to 2,109 manually curated pairwise comparisons from 978 human GEO Curated DataSets, with the complete procedure requiring a mean execution time of 190 seconds across 100 randomly selected target genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:24564370", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "To accomplish this task, we used the 978 human GEO Curated DataSets and we manually performed the selection of 2,109 pair-wise comparisons based on their biological rationale."}, {"asset": "source:pmid:24564370", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Our application uses a customized Association Rule Mining (ARM) algorithm to identify sets of genes showing expression profiles correlated with a gene of interest."}, {"asset": "source:pmid:24564370", "locator": "sections[6] \"CorrelaGenes performances\" paragraph 2", "quote": "Averaging on the considered 100 genes, the whole procedure requires a mean execution time of 190 seconds."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "CorrelaGenes applies a customized Association Rule Mining algorithm to 2,109 manually curated pairwise comparisons from 978 human GEO Curated DataSets, with the complete procedure requiring a mean execution time of 190 seconds across 100 randomly selected target genes.", "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:24564370"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:24564370", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/1471-2105-15-S1-S6", "sha256": "sha256:f188aba2759aeb1b3e9e4d862bdb8155217d64cac36fa3bf25f6de9f8caa8543", "bytes": 30814, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/1471-2105-15-S1-S6", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC4016313", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:24564370:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:70a3fc899ba7272177e264a491d1384aee186f91f42dcda636024ed1edc90926", "bytes": 671, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[4]/table-wrap[1]#T1", "version": null, "note": "Description of the values used in the calculation of ARM indexes", "structure": {"pmcid": "PMC4016313", "section_index": 4, "section_title": "ARM algorithm", "row_count": 6, "column_count": 2}, "table": {"label": "Table 1", "caption": "Description of the values used in the calculation of ARM indexes", "rows": [["Value", "Description"], ["NTp", "Number of comparisons where the target gene is present"], ["NTp, Xp", "Number of comparisons where both the target gene and gene X are present"], ["NTm, Xm", "Number of comparisons where the target gene and the gene X are both modulated"], ["NTm, Xp", "Number of comparisons where the target gene is modulated and the gene X is present"], ["NTp, Xm", "Number of comparisons where the target gene is present and the gene X is modulated"]], "row_count": 6, "column_count": 2, "numeric_cells": 0, "header_rows": 1, "locator": "section[4]/table-wrap[1]#T1"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We perform a χ2 test to evaluate the number of times the target gene and gene X result simultaneously differentially expressed compared to the expected value in the target population."}}], "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["chi_square_test"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: chi_square."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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