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Evaluation of hierarchical models for integrative genomic analyses

Denis Marie, Tadesse Mahlet. 2016. Evaluation of hierarchical models for integrative genomic analyses. Bioinformatics, 32 (5) : pp. 738-746.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Url - jeu de données : https://github.com/mgt000/IntegrativeAnalysis / Url - jeu de données : https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp

Quartile : Outlier, Sujet : MATHEMATICAL & COMPUTATIONAL BIOLOGY / Quartile : Outlier, Sujet : BIOCHEMICAL RESEARCH METHODS / Quartile : Outlier, Sujet : BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Abstract : Motivation: Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data. Results: Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to genomic datasets from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serous cystadenocarcinoma. The integrative models lead to improved model fit and predictive performance. They also provide a better understanding of the biological mechanisms underlying patients' survival. (Résumé d'auteur)

Mots-clés Agrovoc : Modèle de simulation, Modèle mathématique, Bioinformatique, Génie génétique, Méthode statistique, Maladie de l'homme, Cerveau, Maladie de l'appareil génital fém, Néoplasme, Adénome, génomique, Étude de cas, Marqueur génétique, Phénotype, Biologie moléculaire, Analyse de données, Méthodologie, Évaluation

Mots-clés complémentaires : Algorithme

Mots-clés libres : Penalized likelihood methods, Expectation-maximization (EM) algorithms, -omic data

Classification Agris : U10 - Computer science, mathematics and statistics
000 - Other themes
L73 - Animal diseases
U30 - Research methods

Champ stratégique Cirad : Hors axes (2014-2018)

Auteurs et affiliations

  • Denis Marie, CIRAD-BIOS-UMR AGAP (FRA)
  • Tadesse Mahlet, CIRAD-ES-UPR BSef (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/580168/)

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