SEMtree: tree-based structure learning methods with structural equation models.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37294820", "kind": "science-record", "revision": 3, "content_hash": "sha256:a4749b1ce7bdd0aa87fd1314ae5cee15337e16b5a2ee61f94181fe79398d8b7f", "created_at": "2026-11-01T19:02:01Z", "updated_at": "2026-11-01T22:09:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SEMtree: tree-based structure learning methods with structural equation models.", "doi": "10.1093/bioinformatics/btad377", "pmid": "37294820", "authors": ["Mario Grassi", "Barbara Tarantino"], "journal": "Bioinformatics (Oxford, England)", "year": 2023, "abstract": "MOTIVATION: With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework.\nRESULTS: Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance.\nAVAILABILITY AND IMPLEMENTATION: SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.", "claims": [{"id": "claim-method", "statement": "We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37294820", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Code to reproduce all results of the analysis, together with the COVID-19 data used in this study can be found in the supplementary files available at: https://github.com/fernandoPalluzzi/SEMgraph/tree/master/SEMtree. All additional information and results about the analysis are listed in Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37294820", "locator": "sections[20] \"Data availability\" paragraphs 1-3", "quote": "Code to reproduce all results of the analysis, together with the COVID-19 data used in this study can be found in the supplementary files available at: https://github.com/fernandoPalluzzi/SEMgraph/tree/master/SEMtree. All additional information and results about the analysis are listed in Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ST methods show the best performance in identifying COVID-19-related genes, with the highest gene F1-score (0.12 for STr2z and 0.11 for ST) among all the considered methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37294820", "locator": "sections[11] \"4.1 Benchmark results\" paragraph 7", "quote": "ST methods show the best performance in identifying COVID-19-related genes, with the highest gene F1-score (0.12 for STr2z and 0.11 for ST) among all the considered methods."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ST methods show the best performance in identifying COVID-19-related genes, with the highest gene F1-score (0.12 for STr2z and 0.11 for ST) among all the considered methods.", "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:37294820"], "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:GSE172114"], "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:GSE172114"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37294820", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btad377", "sha256": "sha256:86e77da9b2e8bf674dadf0a10f8af46000083cdbb5c059b2d64f7f0b9a20f49d", "bytes": 38498, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btad377", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10287946", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE172114", "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=GSE172114", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE172114", "version": null, "note": "Code to reproduce all results of the analysis, together with the COVID-19 data used in this study can be found in the supplementary files available at: https://github.com/fernandoPalluzzi/SEMgraph/tree/master/SEMtree. 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