{
  "abstract": "In the context of the dynGENIE3 (Anh Huynh-Thu and Geurts 2018 Sci. Rep. 8 3384) approach for inferring regulatory network interactions from time-series data, we show that it is possible to modify that algorithm to significantly enhance its prediction reliability. To quantify the level of reliability, we used ground-zero truths based on simulated datasets generated by the GeneNetWeaver (Schaffter et al 2011 Bioinformatics 27 2263-70) tool. Our work introduces novel methods leveraging time-lagged correlations and estimators of mRNA decay rates, leading to significantly improved driver-target inference. Additionally, a temperature-based rescaling of priors was developed to further enhance prediction reliability. Results demonstrate substantial improvements in performance with a particularly notable increase in area under the precision recall curve scores. These advances underscore the possible gains resulting from incorporating priors into gene regulatory network inference.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Mattia Greco"
    },
    {
      "name": "Federico Ricci-Tersenghi"
    },
    {
      "name": "Olivier C Martin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9t4sqnwatwvf3.bib",
  "cited_by": 0,
  "doi": "10.1088/2632-2153/ae9027",
  "fulltext_url": null,
  "id": "9t4sqnwatwvf3",
  "issns": [
    "2632-2153",
    "2632-2153"
  ],
  "landing_url": "https://hal.science/hal-05762809v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.phys"
    ],
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7170243535",
  "pdf_url": "https://hal.science/hal-05762809/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-05",
  "publisher": "IOP Publishing Ltd",
  "ris": "https://research.draconitic.com/paper/9t4sqnwatwvf3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:40Z",
      "native_id": "hal-05762809",
      "source": "hal",
      "url": "https://hal.science/hal-05762809v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Gene Regulatory Network Analysis",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Boosting reliability when inferring interactions from time-series data in gene regulatory networks",
  "title_text": "Boosting reliability when inferring interactions from time-series data in gene regulatory networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:40Z",
  "url": "https://research.draconitic.com/paper/9t4sqnwatwvf3",
  "venue": "Machine Learning : Science and Technology",
  "versions": []
}