2004/01/30 by Ramon Diaz-Uriarte, Ramón Díaz‐Uriarte, Diaz-Uriarte, Ramon
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Quantitative Methods (q-bio.QM) #q-bio.GN #q-bio.QM
paper · pdf · doi:10.48550/arxiv.q-bio/0401043
Main changes from previous version: - shortened title - shortened paper; moved a lot of extra material to supplementary material - more focus on biological interpretation - more focus on stability and added bootstrap results
openalex publication_date 2004/01/30 · arxiv created 2004/10/08 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivation: ``Molecular signatures'' or ``gene-expression signatures'' are used to predict patients' characteristics using data from coexpressed genes. Signatures can enhance understanding about biological mechanisms and have diagnostic use. However, available methods to search for signatures fail to address key requirements of signatures, especially the discovery of sets of tightly coexpressed genes. Results: After suggesting an operational definition of signature, we develop a method that fulfills these requirements, returning sets of tightly coexpressed genes with good predictive performance. This method can also identify when the data are inconsistent with the hypothesis of a few, stable, easily interpretable sets of coexpressed genes. Identification of molecular signatures in some widely used data sets is questionable under this simple model, which emphasizes the needed for further work on the operationalization of the biological model and the assessment of the stability of putative signatures. Availability: The code (R with C++) is available from http://www.ligarto.org/rdiaz/Software/Software.html under the GNU GPL.