@inproceedings{9dc1ce577e0e4ebcb4cf56c39073bb0b,
title = "Virtual gene: Using correlations between genes to select informative genes on microarray datasets",
abstract = "Gene Selection is one class of most used data analysis algorithms on microarray datasets. The goal of gene selection algorithms is to filter out a small set of informative genes that best explains experimental variations. Traditional gene selection algorithms are mostly single-gene based. Some discriminative scores are calculated and sorted for each gene. Top ranked genes are then selected as informative genes for further study. Such algorithms ignore completely correlations between genes, although such correlations is widely known. Genes interact with each other through various pathways and regulative networks. In this paper, we propose to use, instead of ignoring, such correlations for gene selection. Experiments performed on three public available datasets show promising results.",
author = "Xian Xu and Aidong Zhang",
year = "2005",
doi = "10.1007/11567752\_10",
language = "English",
isbn = "3540294015",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "138--152",
booktitle = "Transactions on Computational Systems Biology II",
address = "Germany",
note = "International Workshop on Bioinformatics Research and Applications, IWBRA 2005 ; Conference date: 22-05-2005 Through 24-05-2005",
}