Tadesse Mahlet G., Denis Marie.
2017. Integrative genomic analyses.
In : ENAR 2017 Spring Meeting abstracts. Eastern North American Region International Biometric Society
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Published version
- Anglais
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Abstract : Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type provides a snapshot of the molecular processes involved in a particular phenotype. While studies focused on one type of -omic data have led to significant results, an integrative -omic analysis can provide a better understanding of the complex biological mechanisms involved in the etiology or progression of a disease by combining the complementary information from each data type. We investigated flexible modeling approaches under different biological relationship scenarios between the various data sources and evaluated their effects on a clinical outcome using data from the Cancer Genome Atlas project. The integrative models led to improved model fit and predictive performance. However, a systematic integration that allows for all possible links between biological features is not necessarily the best approach. (Résumé d'auteur)
Classification Agris : U10 - Computer science, mathematics and statistics
000 - Other themes
L73 - Animal diseases
L10 - Animal genetics and breeding
Auteurs et affiliations
- Tadesse Mahlet G., Georgetown University (USA)
- Denis Marie, CIRAD-BIOS-UMR AGAP (FRA)
Source : Cirad-Agritrop (https://agritrop.cirad.fr/586790/)
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