{
  "abstract": "Cancer heterogeneity and the emergence of innovative therapeutic alternatives have made the development of tools to help in clinical decision making even more necessary. The recent advances in the field of artificial-intelligence-based predictive models make them promising allies due to their ability to integrate vast amounts of descriptors and data, notably omic data, for grasping cancer complexity. However, it is difficult to present a complete picture of this field due to the increasing number of models and the many possible applications. The aim of this review was to offer an overview of omic-based predictive models in oncology through the prism of three important concepts: the objective, the data, and the model. We show that the consideration of these three aspects could help to assess the feasibility, validity and usability of a model and to guide its construction. Lastly, we highlight that taking into account the underlying technical, physiological and biological phenomena inherent to omic data at each step of the project may improve the model predictive capability and its explainability.",
  "abstract_shown": true,
  "affiliations": [
    "IRCM, Université de Montpellier, ICM, INSERM, F-34298 Montpellier, France.",
    "Department of Dermatology, Saint Eloi Hospital, Montpellier University Hospital, 34090 Montpellier, France.",
    "Institut du Cancer de Montpellier, Département D'Oncologie Médicale, F-34298 Montpellier, France.",
    "Université Toulouse Capitole, IRIT, (CNRS/UMR 5505), 31042 Toulouse, France.",
    "IRCM, Université de Montpellier, ICM, INSERM, CNRS, F-34298 Montpellier, France."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Arnaud Nativel",
      "orcid": "0009-0009-7962-9234"
    },
    {
      "affs": [
        0
      ],
      "name": "Sarah Dandou"
    },
    {
      "affs": [
        0
      ],
      "name": "Florian Favier",
      "orcid": "0009-0006-7899-5812"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Candice Lesage",
      "orcid": "0000-0003-4353-6158"
    },
    {
      "affs": [
        0
      ],
      "name": "Alain Mangé",
      "orcid": "0000-0002-1566-9407"
    },
    {
      "affs": [
        3
      ],
      "name": "Julien Aligon",
      "orcid": "0000-0002-1954-8733"
    },
    {
      "affs": [
        4
      ],
      "name": "Peter J Coopman",
      "orcid": "0000-0003-3720-523X"
    },
    {
      "affs": [
        0
      ],
      "name": "Romain M Larive",
      "orcid": "0000-0001-8479-209X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/acym44f0n444c.bib",
  "cited_by": 0,
  "doi": "10.3390/genes17101231",
  "fulltext_url": "https://europepmc.org/articles/PMC13704117",
  "id": "acym44f0n444c",
  "issns": [
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  ],
  "landing_url": "https://hal.umontpellier.fr/hal-05782911v1",
  "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": [
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  "pdf_url": "https://hal.umontpellier.fr/hal-05782911/document",
  "pmcid": "PMC13704117",
  "pmid": "42938347",
  "published": "2026-10-04",
  "publisher": "MDPI",
  "ris": "https://research.draconitic.com/paper/acym44f0n444c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:50Z",
      "native_id": "hal-05782911",
      "source": "hal",
      "url": "https://hal.umontpellier.fr/hal-05782911v1"
    },
    {
      "first_seen": "2026-10-30T12:22:24Z",
      "native_id": "MED:42938347",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42938347"
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  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.bibs",
      "subfield": "bioinformatics"
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    {
      "field": "mathematics",
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    },
    {
      "field": "medicine",
      "origin": "hal:1.sdv.can",
      "subfield": "oncology"
    }
  ],
  "title": "Integration of Multi-Omics in AI-Based Models for Predictive Oncology",
  "title_text": "Integration of Multi-Omics in AI-Based Models for Predictive Oncology",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:24Z",
  "url": "https://research.draconitic.com/paper/acym44f0n444c",
  "venue": "Genes",
  "versions": []
}