Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33240201", "kind": "science-record", "revision": 8, "content_hash": "sha256:297fcca16039135ca02983e3bbd5bbb3ad0029393d54321f9aacb7228d61d248", "created_at": "2026-11-01T15:41:17Z", "updated_at": "2026-11-01T22:45:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study.", "doi": "10.3389/fneur.2020.575376", "pmid": "33240201", "authors": ["Cyril Dargazanli", "Emma Zub", "Jeremy Deverdun", "Mathilde Decourcelle", "Frédéric de Bock", "Julien Labreuche", "Pierre-Henri Lefèvre", "Grégory Gascou", "Imad Derraz", "Carlos Riquelme Bareiro", "Federico Cagnazzo", "Alain Bonafé", "Philippe Marin", "Vincent Costalat", "Nicola Marchi"], "journal": "Frontiers in neurology", "year": 2020, "abstract": "Objective: Mechanical retrieval of thrombotic material from acute ischemic stroke patients provides a unique entry point for translational research investigations. Here, we resolved the proteomes of cardioembolic and atherothrombotic cerebrovascular human thrombi and applied an artificial intelligence routine to examine protein signatures between the two selected groups. Methods: We specifically used n = 32 cardioembolic and n = 28 atherothrombotic diagnosed thrombi from patients suffering from acute stroke and treated by mechanical thrombectomy. Thrombi proteins were successfully separated by gel-electrophoresis. For each thrombi, peptide samples were analyzed by nano-flow liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) to obtain specific proteomes. Relative protein quantification was performed using a label-free LFQ algorithm and all dataset were analyzed using a support-vector-machine (SVM) learning method. Data are available via ProteomeXchange with identifier PXD020398. Clinical data were also analyzed using SVM, alone or in combination with the proteomes. Results: A total of 2,455 proteins were identified by nano-LC-MS/MS in the samples analyzed, with 438 proteins constantly detected in all samples. SVM analysis of LFQ proteomic data delivered combinations of three proteins achieving a maximum of 88.3% for correct classification of the cardioembolic and atherothrombotic samples in our cohort. The coagulation factor XIII appeared in all of the SVM protein trios, associating with cardioembolic thrombi. A combined SVM analysis of the LFQ proteome and clinical data did not deliver a better discriminatory score as compared to the proteome only. Conclusion: Our results advance the portrayal of the human cerebrovascular thrombi proteome. The exploratory SVM analysis outlined sets of proteins for a proof-of-principle characterization of our cohort cardioembolic and atherothrombotic samples. The integrated analysis proposed herein could be further developed and retested on a larger patients population to better understand stroke origin and the associated cerebrovascular pathophysiology.", "claims": [{"id": "claim-method", "statement": "A support-vector machine (SVM) approach was implemented using MATLAB (r2018a, MathWorks, Natick, MA, USA). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD020398.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33240201", "locator": "sections[9] \"Data Analysis\" paragraph 4", "quote": "A support-vector machine (SVM) approach was implemented using MATLAB (r2018a, MathWorks, Natick, MA, USA)."}, {"asset": "source:pmid:33240201", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD020398."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD020398.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33240201", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD020398."}], "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:33240201"], "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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Associates with calmodulin", "0.1326"], ["2", "• Coagulation factor XIII", "Catalyzes the last step of the coagulation cascade by crosslinking fibrin fibers", "0.0022"], ["", "• F-actin-capping protein", "Binds to the fast-growing ends of actin filaments in a Ca2+ independent manner", "0.1752"], ["", "• Myosin light chain kinase", "Calcium/calmodulin-dependent kinase implicated in smooth muscle contraction via phosphorylation of myosin light chains", "0.0142"], ["3", "• Coagulation factor XIII", "Catalyzes the last step of the coagulation cascade by crosslinking fibrin fibers", "0.0022"], ["", "• Septin-7", "Filament-forming cytoskeletal GTPase. 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