dorsal/arxiv
View SchemaMolecular Signatures from Gene Expression Data
| Authors | Ramon Diaz-Uriarte |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0401043 |
| URL | https://arxiv.org/abs/q-bio/0401043 |
Abstract
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.
{
"annotation_id": "6ea14c91-d8c5-4d4a-9910-1e1f75ce3350",
"date_created": "2026-03-02T18:01:31.821000Z",
"date_modified": "2026-03-02T18:01:31.821000Z",
"file_hash": "bbff57821cbe23a2f3a64881f8d8ffa57377be7d5ebd4c9dd811fc99818ba3f1",
"private": false,
"record": {
"abstract": "Motivation: ``Molecular signatures\u0027\u0027 or ``gene-expression signatures\u0027\u0027 are\nused to predict patients\u0027 characteristics using data from coexpressed genes.\nSignatures can enhance understanding about biological mechanisms and have\ndiagnostic use. However, available methods to search for signatures fail to\naddress key requirements of signatures, especially the discovery of sets of\ntightly coexpressed genes. Results: After suggesting an operational definition\nof signature, we develop a method that fulfills these requirements, returning\nsets of tightly coexpressed genes with good predictive performance. This method\ncan also identify when the data are inconsistent with the hypothesis of a few,\nstable, easily interpretable sets of coexpressed genes. Identification of\nmolecular signatures in some widely used data sets is questionable under this\nsimple model, which emphasizes the needed for further work on the\noperationalization of the biological model and the assessment of the stability\nof putative signatures. Availability: The code (R with C++) is available from\nhttp://www.ligarto.org/rdiaz/Software/Software.html under the GNU GPL.",
"arxiv_id": "q-bio/0401043",
"authors": [
"Ramon Diaz-Uriarte"
],
"categories": [
"q-bio.QM",
"q-bio.GN"
],
"title": "Molecular Signatures from Gene Expression Data",
"url": "https://arxiv.org/abs/q-bio/0401043"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "ef1def59-f20e-4e9b-bb52-7fca3ba85800",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}