{"corr_author":"1","month":"10","article_processing_charge":"Yes","oa_version":"Published Version","quality_controlled":"1","author":[{"id":"81b43fb8-c9d5-11ef-bf68-ade532a1f204","last_name":"Zhang","full_name":"Zhang, Chen Y","first_name":"Chen Y"},{"first_name":"Angelo","full_name":"Rosa, Angelo","last_name":"Rosa"},{"last_name":"Sanguinetti","full_name":"Sanguinetti, Guido","first_name":"Guido"}],"article_type":"original","external_id":{"arxiv":["2409.14425"]},"title":"bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction","article_number":"043006","date_published":"2025-10-21T00:00:00Z","abstract":[{"lang":"eng","text":"The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing seven distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data."}],"DOAJ_listed":"1","citation":{"chicago":"Zhang, Chen Y, Angelo Rosa, and Guido Sanguinetti. “BioSBM: A Random Graph Model to Integrate Epigenomic Data in Chromatin Structure Prediction.” <i>PRX Life</i>. American Physical Society, 2025. <a href=\"https://doi.org/10.1103/gy1p-4256\">https://doi.org/10.1103/gy1p-4256</a>.","ista":"Zhang CY, Rosa A, Sanguinetti G. 2025. bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction. PRX Life. 3(4), 043006.","ieee":"C. Y. Zhang, A. Rosa, and G. Sanguinetti, “bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction,” <i>PRX Life</i>, vol. 3, no. 4. American Physical Society, 2025.","mla":"Zhang, Chen Y., et al. “BioSBM: A Random Graph Model to Integrate Epigenomic Data in Chromatin Structure Prediction.” <i>PRX Life</i>, vol. 3, no. 4, 043006, American Physical Society, 2025, doi:<a href=\"https://doi.org/10.1103/gy1p-4256\">10.1103/gy1p-4256</a>.","ama":"Zhang CY, Rosa A, Sanguinetti G. bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction. <i>PRX Life</i>. 2025;3(4). doi:<a href=\"https://doi.org/10.1103/gy1p-4256\">10.1103/gy1p-4256</a>","short":"C.Y. Zhang, A. Rosa, G. Sanguinetti, PRX Life 3 (2025).","apa":"Zhang, C. Y., Rosa, A., &#38; Sanguinetti, G. (2025). bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction. <i>PRX Life</i>. American Physical Society. <a href=\"https://doi.org/10.1103/gy1p-4256\">https://doi.org/10.1103/gy1p-4256</a>"},"file_date_updated":"2026-02-18T07:57:39Z","ddc":["570"],"publication_status":"published","OA_type":"gold","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2025","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"publication_identifier":{"issn":["2835-8279"]},"language":[{"iso":"eng"}],"acknowledgement":"G.S. acknowledges co-funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE1 - Project FAIR “Future Artificial Intelligence Research”. This resource was co-financed by the Next Generation EU [DM 1555 del 11.10.22]. A.R. acknowledges financial support from PNRR Grant CN 00000013 CN-HPC, M4C2I1.4, spoke 7, funded by Next Generation EU.","doi":"10.1103/gy1p-4256","publication":"PRX Life","date_created":"2026-02-17T07:53:01Z","file":[{"success":1,"date_created":"2026-02-18T07:57:39Z","checksum":"76ddfee3efdb4c9d085059b5a142ed78","file_name":"2025_PRXLife_Zhang.pdf","date_updated":"2026-02-18T07:57:39Z","content_type":"application/pdf","file_id":"21314","relation":"main_file","file_size":1888053,"creator":"dernst","access_level":"open_access"}],"_id":"21269","publisher":"American Physical Society","PlanS_conform":"1","issue":"4","status":"public","volume":3,"has_accepted_license":"1","date_updated":"2026-02-18T08:01:00Z","day":"21","OA_place":"publisher","arxiv":1,"oa":1,"type":"journal_article","intvolume":"         3","department":[{"_id":"GaTk"}]}