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Using biological constraints to improve prediction in precision oncology

Omar, Mohamed ; Dinalankara, Wikum ; Mulder, Lotte ; Coady, Tendai ; Zanettini, Claudio ; Imada, Eddie Luidy ; Younes, Laurent ; Geman, Donald ; Marchionni, Luigi

iScience, 2023-03, Vol.26 (3), p.106108-106108, Article 106108 [Periódico revisado por pares]

United States: Elsevier Inc

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  • Título:
    Using biological constraints to improve prediction in precision oncology
  • Autor: Omar, Mohamed ; Dinalankara, Wikum ; Mulder, Lotte ; Coady, Tendai ; Zanettini, Claudio ; Imada, Eddie Luidy ; Younes, Laurent ; Geman, Donald ; Marchionni, Luigi
  • Assuntos: Cancer ; Machine learning ; Omics ; Precision medicine
  • É parte de: iScience, 2023-03, Vol.26 (3), p.106108-106108, Article 106108
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
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  • Descrição: Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle-invasive disease, response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training to features capturing specific biological mechanisms; and agnostic, in which the training did not use any a priori biological information. Mechanistic models had a similar or better testing performance than their agnostic counterparts, with enhanced interpretability. Our findings support the use of biological constraints to develop robust gene signatures with high translational potential. [Display omitted] •Most gene signatures suffer from overfitting and limited interpretability•Using known biology in the training can yield robust mechanistic classifiers•We compared the performance of mechanistic models to standard agnostic ones•Mechanistic models tend to have robust performance with enhanced interpretability Precision medicine; Cancer; Omics; Machine learning.
  • Editor: United States: Elsevier Inc
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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