{
  "abstract": "Identifying regulatory gene interactions is inherently challenging as it depends on both the transcriptional and epigenetic landscapes. Multiple methods are proposed to infer gene regulatory networks (GRNs) using single-cell multi-omics data, yet the absence of a standardized benchmarking framework remains an obstacle to unbiased performance evaluation. Here we present BEAR-GRN, a systematic resource providing standardized datasets, curated ground-truth networks, and a unified computational pipeline for benchmarking GRN inference methods. Using BEAR-GRN, we assess state-of-the-art multi-omics GRN inference methods across paired single-cell RNA-seq and ATAC-seq datasets spanning human and mouse cell types. We evaluate accuracy against four complementary ground-truth definitions, alongside computational efficiency, output stability, and modality contribution. Although ground-truth choice profoundly influences performance, LINGER and DIRECT-NET show the highest overall accuracy and stability. Modality perturbation experiments reveal that current methods are predominantly RNA-driven, with chromatin accessibility contributing limited independent signal. BEAR-GRN enables researchers to evaluate existing and new GRN methods against established baselines.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, PA, USA.",
    "Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, PA, USA. yuzun@pennstatehealth.psu.edu.",
    "Department of Molecular and Precision Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA. yuzun@pennstatehealth.psu.edu."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Karamveer Karamveer",
      "orcid": "0000-0002-5339-3317"
    },
    {
      "affs": [
        0
      ],
      "name": "Eric Moeller"
    },
    {
      "affs": [
        0
      ],
      "name": "Hannah Valensi"
    },
    {
      "affs": [
        0
      ],
      "name": "Ewura-Esi Manful",
      "orcid": "0000-0001-8068-3697"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Yasin Uzun",
      "orcid": "0000-0003-3478-3499"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7q4s5g9mhw46s.bib",
  "cited_by": 0,
  "doi": "10.1038/s41467-026-77838-w",
  "fulltext_url": "https://europepmc.org/articles/PMC13683341",
  "id": "7q4s5g9mhw46s",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://europepmc.org/article/MED/42901905",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7213535722",
  "pdf_url": "https://europepmc.org/articles/PMC13683341?pdf=render",
  "pmcid": "PMC13683341",
  "pmid": "42901905",
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7q4s5g9mhw46s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:41Z",
      "native_id": "MED:42901905",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42901905"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Gene Regulatory Network Analysis",
      "subfield": "molecular-biology"
    }
  ],
  "title": "BEAR-GRN: Systematic assessment of single-cell multi-omics-based gene regulatory network inference methods.",
  "title_text": "BEAR-GRN: Systematic assessment of single-cell multi-omics-based gene regulatory network inference methods.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:41Z",
  "url": "https://research.draconitic.com/paper/7q4s5g9mhw46s",
  "venue": "Nature communications",
  "versions": []
}