Evaluation of hierarchical models for integrative genomic analyses

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.

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Bibliographic Details
Main Authors: Denis, Marie, Tadesse, Mahlet
Format: article biblioteca
Language:eng
Subjects:U10 - Informatique, mathématiques et statistiques, S50 - Santé humaine, 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, contrôle continu, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_37958, http://aims.fao.org/aos/agrovoc/c_15974, http://aims.fao.org/aos/agrovoc/c_7377, http://aims.fao.org/aos/agrovoc/c_29198, http://aims.fao.org/aos/agrovoc/c_9713, http://aims.fao.org/aos/agrovoc/c_2849, http://aims.fao.org/aos/agrovoc/c_5122, http://aims.fao.org/aos/agrovoc/c_121, http://aims.fao.org/aos/agrovoc/c_92382, http://aims.fao.org/aos/agrovoc/c_24392, http://aims.fao.org/aos/agrovoc/c_24030, http://aims.fao.org/aos/agrovoc/c_5776, http://aims.fao.org/aos/agrovoc/c_4891, http://aims.fao.org/aos/agrovoc/c_15962, http://aims.fao.org/aos/agrovoc/c_12522, http://aims.fao.org/aos/agrovoc/c_2736,
Online Access:http://agritrop.cirad.fr/580168/
http://agritrop.cirad.fr/580168/1/2015_Bioinformatics_Denis_Tadesse.pdf
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spelling dig-cirad-fr-5801682024-04-24T11:30:09Z http://agritrop.cirad.fr/580168/ http://agritrop.cirad.fr/580168/ Evaluation of hierarchical models for integrative genomic analyses. Denis Marie, Tadesse Mahlet. 2016. Bioinformatics, 32 (5) : 738-746.https://doi.org/10.1093/bioinformatics/btv653 <https://doi.org/10.1093/bioinformatics/btv653> Evaluation of hierarchical models for integrative genomic analyses Denis, Marie Tadesse, Mahlet eng 2016 Bioinformatics U10 - Informatique, mathématiques et statistiques S50 - Santé humaine 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 contrôle continu http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_37958 http://aims.fao.org/aos/agrovoc/c_15974 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_29198 http://aims.fao.org/aos/agrovoc/c_9713 http://aims.fao.org/aos/agrovoc/c_2849 http://aims.fao.org/aos/agrovoc/c_5122 http://aims.fao.org/aos/agrovoc/c_121 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_5776 http://aims.fao.org/aos/agrovoc/c_4891 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_2736 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/580168/1/2015_Bioinformatics_Denis_Tadesse.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1093/bioinformatics/btv653 10.1093/bioinformatics/btv653 info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btv653 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1093/bioinformatics/btv653 info:eu-repo/semantics/reference/purl/https://github.com/mgt000/IntegrativeAnalysis info:eu-repo/semantics/dataset/purl/https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic U10 - Informatique, mathématiques et statistiques
S50 - Santé humaine
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
contrôle continu
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_24199
http://aims.fao.org/aos/agrovoc/c_37958
http://aims.fao.org/aos/agrovoc/c_15974
http://aims.fao.org/aos/agrovoc/c_7377
http://aims.fao.org/aos/agrovoc/c_29198
http://aims.fao.org/aos/agrovoc/c_9713
http://aims.fao.org/aos/agrovoc/c_2849
http://aims.fao.org/aos/agrovoc/c_5122
http://aims.fao.org/aos/agrovoc/c_121
http://aims.fao.org/aos/agrovoc/c_92382
http://aims.fao.org/aos/agrovoc/c_24392
http://aims.fao.org/aos/agrovoc/c_24030
http://aims.fao.org/aos/agrovoc/c_5776
http://aims.fao.org/aos/agrovoc/c_4891
http://aims.fao.org/aos/agrovoc/c_15962
http://aims.fao.org/aos/agrovoc/c_12522
http://aims.fao.org/aos/agrovoc/c_2736
U10 - Informatique, mathématiques et statistiques
S50 - Santé humaine
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
contrôle continu
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_24199
http://aims.fao.org/aos/agrovoc/c_37958
http://aims.fao.org/aos/agrovoc/c_15974
http://aims.fao.org/aos/agrovoc/c_7377
http://aims.fao.org/aos/agrovoc/c_29198
http://aims.fao.org/aos/agrovoc/c_9713
http://aims.fao.org/aos/agrovoc/c_2849
http://aims.fao.org/aos/agrovoc/c_5122
http://aims.fao.org/aos/agrovoc/c_121
http://aims.fao.org/aos/agrovoc/c_92382
http://aims.fao.org/aos/agrovoc/c_24392
http://aims.fao.org/aos/agrovoc/c_24030
http://aims.fao.org/aos/agrovoc/c_5776
http://aims.fao.org/aos/agrovoc/c_4891
http://aims.fao.org/aos/agrovoc/c_15962
http://aims.fao.org/aos/agrovoc/c_12522
http://aims.fao.org/aos/agrovoc/c_2736
spellingShingle U10 - Informatique, mathématiques et statistiques
S50 - Santé humaine
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
contrôle continu
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_24199
http://aims.fao.org/aos/agrovoc/c_37958
http://aims.fao.org/aos/agrovoc/c_15974
http://aims.fao.org/aos/agrovoc/c_7377
http://aims.fao.org/aos/agrovoc/c_29198
http://aims.fao.org/aos/agrovoc/c_9713
http://aims.fao.org/aos/agrovoc/c_2849
http://aims.fao.org/aos/agrovoc/c_5122
http://aims.fao.org/aos/agrovoc/c_121
http://aims.fao.org/aos/agrovoc/c_92382
http://aims.fao.org/aos/agrovoc/c_24392
http://aims.fao.org/aos/agrovoc/c_24030
http://aims.fao.org/aos/agrovoc/c_5776
http://aims.fao.org/aos/agrovoc/c_4891
http://aims.fao.org/aos/agrovoc/c_15962
http://aims.fao.org/aos/agrovoc/c_12522
http://aims.fao.org/aos/agrovoc/c_2736
U10 - Informatique, mathématiques et statistiques
S50 - Santé humaine
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
contrôle continu
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_24199
http://aims.fao.org/aos/agrovoc/c_37958
http://aims.fao.org/aos/agrovoc/c_15974
http://aims.fao.org/aos/agrovoc/c_7377
http://aims.fao.org/aos/agrovoc/c_29198
http://aims.fao.org/aos/agrovoc/c_9713
http://aims.fao.org/aos/agrovoc/c_2849
http://aims.fao.org/aos/agrovoc/c_5122
http://aims.fao.org/aos/agrovoc/c_121
http://aims.fao.org/aos/agrovoc/c_92382
http://aims.fao.org/aos/agrovoc/c_24392
http://aims.fao.org/aos/agrovoc/c_24030
http://aims.fao.org/aos/agrovoc/c_5776
http://aims.fao.org/aos/agrovoc/c_4891
http://aims.fao.org/aos/agrovoc/c_15962
http://aims.fao.org/aos/agrovoc/c_12522
http://aims.fao.org/aos/agrovoc/c_2736
Denis, Marie
Tadesse, Mahlet
Evaluation of hierarchical models for integrative genomic analyses
description 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.
format article
topic_facet U10 - Informatique, mathématiques et statistiques
S50 - Santé humaine
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
contrôle continu
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_24199
http://aims.fao.org/aos/agrovoc/c_37958
http://aims.fao.org/aos/agrovoc/c_15974
http://aims.fao.org/aos/agrovoc/c_7377
http://aims.fao.org/aos/agrovoc/c_29198
http://aims.fao.org/aos/agrovoc/c_9713
http://aims.fao.org/aos/agrovoc/c_2849
http://aims.fao.org/aos/agrovoc/c_5122
http://aims.fao.org/aos/agrovoc/c_121
http://aims.fao.org/aos/agrovoc/c_92382
http://aims.fao.org/aos/agrovoc/c_24392
http://aims.fao.org/aos/agrovoc/c_24030
http://aims.fao.org/aos/agrovoc/c_5776
http://aims.fao.org/aos/agrovoc/c_4891
http://aims.fao.org/aos/agrovoc/c_15962
http://aims.fao.org/aos/agrovoc/c_12522
http://aims.fao.org/aos/agrovoc/c_2736
author Denis, Marie
Tadesse, Mahlet
author_facet Denis, Marie
Tadesse, Mahlet
author_sort Denis, Marie
title Evaluation of hierarchical models for integrative genomic analyses
title_short Evaluation of hierarchical models for integrative genomic analyses
title_full Evaluation of hierarchical models for integrative genomic analyses
title_fullStr Evaluation of hierarchical models for integrative genomic analyses
title_full_unstemmed Evaluation of hierarchical models for integrative genomic analyses
title_sort evaluation of hierarchical models for integrative genomic analyses
url http://agritrop.cirad.fr/580168/
http://agritrop.cirad.fr/580168/1/2015_Bioinformatics_Denis_Tadesse.pdf
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