Prediction, prioritization and validation of body fluid biomarkers for cancer and cardiovascular diseases

Fonte Standard Gras
Identifiant

Unité de recherche

INNOSUISSE

Numéro de projet

12396.1;13 PFLS-LS

Titre du projet

Prediction, prioritization and validation of body fluid biomarkers for cancer and cardiovascular diseases

Titre du projet anglais

Prediction, prioritization and validation of body fluid biomarkers for cancer and cardiovascular diseases

Textes relatifs à ce projet

	Allemand	Français	Italien	Anglais
Description succincte	-	-	-
Résumé des résultats (Abstract)	-	-	-

Textes saisis

Catégorie	Texte
Description succincte (Anglais)	Prediction, prioritization and validation of body fluid biomarkers for cancer and cardiovascular diseases
Résumé des résultats (Abstract) (Anglais)	In this follow-up project, we will generate immediate and high commercial value from the previous KTI project by discovering, prioritizing, and validating potential mRNA and protein biomarkers for applications on body fluids. In a first step, we will adjust our existing data mining engine to optimally integrate the biomarker prediction workflow for this project. This will be achieved together with a network of project partners from ETH, USZ, and several start-up companies. In a second step, we will use this pipeline for biomarker discovery in cardiovascular diseases (together with USZ) and cancer (together with ETH, ProteoMediX, Diagnoplex and Biognosys). We show in Appendix II and III several proofs of principle for various scenarios using our existing tools.

Catégorie

Texte

Description succincte
(Anglais)

Prediction, prioritization and validation of body fluid biomarkers for cancer and cardiovascular diseases

Résumé des résultats (Abstract)
(Anglais)

In this follow-up project, we will generate immediate and high commercial value from the previous KTI project by discovering, prioritizing, and validating potential mRNA and protein biomarkers for applications on body fluids. In a first step, we will adjust our existing data mining engine to optimally integrate the biomarker prediction workflow for this project. This will be achieved together with a network of project partners from ETH, USZ, and several start-up companies. In a second step, we will use this pipeline for biomarker discovery in cardiovascular diseases (together with USZ) and cancer (together with ETH, ProteoMediX, Diagnoplex and Biognosys). We show in Appendix II and III several proofs of principle for various scenarios using our existing tools.