Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37816770", "kind": "science-record", "revision": 3, "content_hash": "sha256:3b62ab6ffac7b76e056c95def6a281b7c5b756912dbcccdebc6a3f211affa3e3", "created_at": "2026-11-01T19:02:40Z", "updated_at": "2026-11-01T21:31:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD.", "doi": "10.1038/s41540-023-00311-7", "pmid": "37816770", "authors": ["Klaudia Adamowicz", "Lis Arend", "Andreas Maier", "Johannes R Schmidt", "Bernhard Kuster", "Olga Tsoy", "Olga Zolotareva", "Jan Baumbach", "Tanja Laske"], "journal": "NPJ systems biology and applications", "year": 2023, "abstract": "Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .", "claims": [{"id": "claim-method", "statement": "Upon mapping each study’s IDs to the same gene identifier space, we assess shared IDs, so IDs present in more than one study, with an intersection analysis between the harmonized study results and the published gene lists to demonstrate the benefit of ProHarMeD (Fig. 7).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37816770", "locator": "sections[8] \"Comparison of study results before and after ID harmonization\" paragraph 2", "quote": "Upon mapping each study’s IDs to the same gene identifier space, we assess shared IDs, so IDs present in more than one study, with an intersection analysis between the harmonized study results and the published gene lists to demonstrate the benefit of ProHarMeD (Fig. 7)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The harmonized and not harmonized protein and gene lists20–23 utilized in this study are made available through the source code repository (https://github.com/symbod/proharmed). Additionally, all datasets used in this research have been integrated into the website (see section Implementation). The website allows users to reproduce each result step by step without requiring any additional programmatic knowledge.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37816770", "locator": "sections[27] \"notes\" paragraph 1", "quote": "The harmonized and not harmonized protein and gene lists20–23 utilized in this study are made available through the source code repository (https://github.com/symbod/proharmed). Additionally, all datasets used in this research have been integrated into the website (see section Implementation). The website allows users to reproduce each result step by step without requiring any additional programmatic knowledge."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ID harmonization increased the shared genes between studies by 50%.", "kind": "quantitative", "target": {"value": 50.0, "unit": "per cent", "tolerance": null, "source": "Thanks to ProHarMeD, the intersection size, which is the number of occurrences of the genes in the four studies, for 5 out of the 21 genes was increased (Fig. 7). More importantly, the intersection analysis after harmonization identified 10 additional genes, representing an increase of about 50%."}, "evidence": [{"asset": "source:pmid:37816770", "locator": "sections[8] \"Comparison of study results before and after ID harmonization\" paragraph 4", "quote": "Thanks to ProHarMeD, the intersection size, which is the number of occurrences of the genes in the four studies, for 5 out of the 21 genes was increased (Fig. 7). More importantly, the intersection analysis after harmonization identified 10 additional genes, representing an increase of about 50%."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ID harmonization increased the shared genes between studies by 50%.", "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:37816770"], "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:PXD002498"], "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:PXD002498"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37816770", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41540-023-00311-7", "sha256": "sha256:28d24d7927907da16ccc832b543d7ea55f1c1cc2460219aedfcabba9e2989e8b", "bytes": 42224, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41540-023-00311-7", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10564802", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:PXD002498", "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=PXD002498", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "PXD002498", "version": null, "note": "The harmonized and not harmonized protein and gene lists20–23 utilized in this study are made available through the source code repository (https://github.com/symbod/proharmed). Additionally, all datasets used in this research have been integrated into the website (see section Implementation). The website allows users to reproduce each result step by step without requiring any additional programmatic knowledge.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The harmonized and not harmonized protein and gene lists20–23 utilized in this study are made available through the source code repository (https://github.com/symbod/proharmed). Additionally, all datasets used in this research have been integrated into the website (see section Implementation). The website allows users to reproduce each result step by step without requiring any additional programmatic knowledge.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": "Quantitative proteomics in osteoblasts-released matrix vesicles", "resolution_checked_at": "2026-11-01T19:09:50Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:PXD009434", "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=PXD009434", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "PXD009434", "version": null, "note": "The harmonized and not harmonized protein and gene lists20–23 utilized in this study are made available through the source code repository (https://github.com/symbod/proharmed). Additionally, all datasets used in this research have been integrated into the website (see section Implementation). The website allows users to reproduce each result step by step without requiring any additional programmatic knowledge.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The harmonized and not harmonized protein and gene lists20–23 utilized in this study are made available through the source code repository (https://github.com/symbod/proharmed). Additionally, all datasets used in this research have been integrated into the website (see section Implementation). The website allows users to reproduce each result step by step without requiring any additional programmatic knowledge.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": "Synergistic potential of sulfated hyaluronic acid and dexamethasone", "resolution_checked_at": "2026-11-01T19:09:50Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:PXD002498 not downloaded", "asset:data:geo:PXD002498 licence not read", "asset:data:geo:PXD009434 not downloaded", "asset:data:geo:PXD009434 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "intersection analysis", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Upon mapping each study’s IDs to the same gene identifier space, we assess shared IDs, so IDs present in more than one study, with an intersection analysis between the harmonized study results and the published gene lists to demonstrate the benefit of ProHarMeD (Fig. 7)."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": null, "expected_value": 50.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "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 (intersection analysis), 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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["PXD002498", "PXD009434"], "resolved_accessions": ["PXD002498", "PXD009434"], "statements": ["The harmonized and not harmonized protein and gene lists20–23 utilized in this study are made available through the source code repository (https://github.com/symbod/proharmed). Additionally, all datasets used in this research have been integrated into the website (see section Implementation). The website allows users to reproduce each result step by step without requiring any additional programmatic knowledge."], "basis": "The paper names PXD002498, PXD009434 in ProteomeXchange and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:37816770", "data:geo:PXD002498"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:PXD002498"]}, "gaps": ["method:not_determinable"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:55:29Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11017, "completion_tokens": 1411, "cached_tokens": 264}}, "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:37816770", "type": "paper", "label": "pmid:37816770"}, {"id": "source:pmid:37816770", "type": "manuscript", "label": "PMID 37816770 full text"}, {"id": "data:geo:PXD002498", "type": "dataset", "label": "GEO PXD002498"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37816770", "type": "paper", "label": "pmid:37816770"}, {"id": "source:pmid:37816770", "type": "manuscript", "label": "PMID 37816770 full text"}, {"id": "data:geo:PXD002498", "type": "dataset", "label": "GEO PXD002498"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37816770", "type": "extraction", "used": ["source:pmid:37816770"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-PXD002498", "type": "accession_resolution", "used": ["data:geo:PXD002498"], "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:37816770", "parent_hash": "sha256:a43416f54eac8e2b0a6f4b69679a4464663a884ce063220f6627d0c1e7e4789f", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:PXD002498 licence not read", "asset:data:geo:PXD002498 not downloaded", "asset:data:geo:PXD009434 licence not read", "asset:data:geo:PXD009434 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["ifferent assay systems, such as in vitro and in vivo systems, are used. Finally, even though prot"], "order_basis": "paper_text_order:773", "id": "assay:1"}]}