Agritrop
Home

Integrative genomic analyses

Tadesse Mahlet G., Denis Marie. 2017. Integrative genomic analyses. In : ENAR 2017 Spring Meeting abstracts. Eastern North American Region International Biometric Society. Washington : Eastern North American Region International Biometric Society, Résumé, p. 189. ENAR 2017 Spring Meeting, Washington, États-Unis, 12 March 2017/15 March 2017.

Paper without proceedings
[img]
Preview
Published version - Anglais
Use under authorization by the author or CIRAD.
ID586790.pdf

Télécharger (41kB) | Preview

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/)

View Item (staff only) View Item (staff only)

[ Page générée et mise en cache le 2021-02-02 ]