---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '21269'
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.
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.
article_number: '043006'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Chen Y
  full_name: Zhang, Chen Y
  id: 81b43fb8-c9d5-11ef-bf68-ade532a1f204
  last_name: Zhang
- first_name: Angelo
  full_name: Rosa, Angelo
  last_name: Rosa
- first_name: Guido
  full_name: Sanguinetti, Guido
  last_name: Sanguinetti
citation:
  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>'
  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>'
  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>.'
  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.'
  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.'
  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>.'
  short: C.Y. Zhang, A. Rosa, G. Sanguinetti, PRX Life 3 (2025).
corr_author: '1'
date_created: 2026-02-17T07:53:01Z
date_published: 2025-10-21T00:00:00Z
date_updated: 2026-02-18T08:01:00Z
day: '21'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1103/gy1p-4256
external_id:
  arxiv:
  - '2409.14425'
file:
- access_level: open_access
  checksum: 76ddfee3efdb4c9d085059b5a142ed78
  content_type: application/pdf
  creator: dernst
  date_created: 2026-02-18T07:57:39Z
  date_updated: 2026-02-18T07:57:39Z
  file_id: '21314'
  file_name: 2025_PRXLife_Zhang.pdf
  file_size: 1888053
  relation: main_file
  success: 1
file_date_updated: 2026-02-18T07:57:39Z
has_accepted_license: '1'
intvolume: '         3'
issue: '4'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '10'
oa: 1
oa_version: Published Version
publication: PRX Life
publication_identifier:
  issn:
  - 2835-8279
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
status: public
title: 'bioSBM: A random graph model to integrate epigenomic data in chromatin structure
  prediction'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2025'
...