Refining Gene Selection and Outlier Detection in Glioblastoma Based on a Consensus Approach for Regularized Survival Models | SpringerLink
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Refining Gene Selection and Outlier Detection in Glioblastoma Based on a Consensus Approach for Regularized Survival Models

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Bioinformatics and Biomedical Engineering (IWBBIO 2024)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 14848))

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Abstract

Glioblastoma, the most malignant brain cancer in adults, exhibits vast heterogeneities in prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses, which calls the need to develop personalized therapeutic approaches. The identification of long/ short-term survivors, along with their associated gene expression markers, opens promising avenues for tailored treatments. However, modeling omics data is particularly challenging due to its high-dimensionality. Our study aimed to create survival models using gene expression data retrieved from tumour tissue, with the goal of detecting outlier observations. These observations correspond to glioblastoma patients whose survival time is much greater/smaller than predicted. To assist in dimensionality reduction and select relevant genes, elastic net and network-based regularization were applied. For each method, different outlier observations were obtained. The rank product test was used as a consensus method, enabling the identification of observations whose martingale residuals were consistently large across different models, thus producing a consensual list of outliers.

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Acknowledgments

This work was financed by Fundação para a Ciência e a Tecnologia: UIDB/00006/2020 (DOI:10.54499/ UIDB/00006/2020), PTDC/CCI-BIO/4180/2020 (“MONET - Multi-omic networks in gliomas”, DOI: 10.54499/PTDC/CCI-BIO/4180/2020), UIDB/00297/2020 (DOI: 10.54499/UIDB/00297/2020) and UIDP/00297/2020 (DOI:10.54499/UIDP/00297/2020)(NOVA Math), UIDB/00667/2020 (DOI: 10.54499/UIDB/00667/2020) and UIDP /00667/2020 (DOI:10.54499/UIDP/00667/2020) (UNIDEMI), CEECINST/00042/2021.

Author information

Authors and Affiliations

  1. Departamento de Estatística e Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

    João Brandão & Eunice Carrasquinha

  2. Center for Mathematics and Applications (NOVA Math), Department of Mathematics, NOVA School of Science and Technology (NOVA FCT), Caparica, Portugal

    Marta B. Lopes

  3. UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA FCT, Caparica, Portugal

    Marta B. Lopes

  4. CEAUL - Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

    Eunice Carrasquinha

Authors
  1. João Brandão
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  2. Marta B. Lopes
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  3. Eunice Carrasquinha
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Corresponding author

Correspondence to Eunice Carrasquinha .

Editor information

Editors and Affiliations

  1. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Ignacio Rojas

  2. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Francisco Ortuño

  3. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Fernando Rojas

  4. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Luis Javier Herrera

  5. Department of Applied Mathematics, University of Granada, Granada, Granada, Spain

    Olga Valenzuela

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Brandão, J., Lopes, M.B., Carrasquinha, E. (2024). Refining Gene Selection and Outlier Detection in Glioblastoma Based on a Consensus Approach for Regularized Survival Models. In: Rojas, I., Ortuño, F., Rojas, F., Herrera, L.J., Valenzuela, O. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2024. Lecture Notes in Computer Science(), vol 14848. Springer, Cham. https://doi.org/10.1007/978-3-031-64629-4_2

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  • DOI: https://doi.org/10.1007/978-3-031-64629-4_2

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  • Online ISBN: 978-3-031-64629-4

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