sbv Improver diagnostic signature challenge: Preface to this special issue

Julia Hoeng, Gustavo Stolovitzky, Manuel C. Peitsch

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The task of predicting disease phenotype from gene expression data has been addressed hundreds if not thousands of times in the recent literature. This expanding body of work is not only an indication that the problem is of great importance and general interest, but it also reveals that neither the experimental nor the computational limitations of translating data to disease information have been satisfactorily understood. To contribute to the advancement of the field, promote collaborative thinking and enable a fair and unbiased comparison of methods, IMPRO VER revisited the problem of gene-expression to phenotype prediction using a collaborative-competition paradigm. This special issue of Systems Biomedicine reports the results of the sbv IMPRO VER Diagnostic Signature Challenge designed to identify best analytic approaches to predict phenotype from gene expression data.

Original languageEnglish
Pages (from-to)193-195
Number of pages3
JournalSystems Biomedicine
Volume1
Issue number4
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • sbv Improver

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