@inproceedings{cc8d9602501b4f79aaa0b62e441f7ee2,
title = "Identification of phenotype-defining gene signatures using the gene-pair matrix based clustering",
abstract = "Mining the {"}meaningful{"} clues from vast amount of expression profiling data remains to be challenge for biologists. After all the statistical tests, biologists often struggle deciding how to do next with a large list of genes without any obvious theme of mechanism, partly because most statistical analyses do not incorporate understanding of biological systems before hand. Here, we developed a novel method of {"}gene -pair difference within a sample{"} to identify phenotype-defining gene signatures, based on the hypothesis that a biological state is governed by the relative difference among different biological processes. For gene expression, it is relative difference among the genes within a sample (an individual, cell, etc), the highest frequency of occurrences a gene contributing to the within sample difference underline the contributions of genes in defining the biological states. We tested the method on three datasets, and identified the most important gene-pairs to drive the phenotypic differences.",
keywords = "Gene pair, Hierarchical clustering, Lymphomas and adenocarcinoma, Phenotype-defining gene signatures",
author = "Lee, {Chung Wein} and Li, {Shuyu Dan} and Su, {Eric W.} and Birong Liao",
year = "2006",
doi = "10.1007/11960669_10",
language = "English",
isbn = "3540689702",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "106--119",
booktitle = "Data Mining and Bioinformatics - First International Workshop, VDMB 2006, Revised Selected Papers",
address = "Germany",
note = "1st International Workshop on Data Mining and Bioinformatics, VDMB 2006 ; Conference date: 11-09-2006 Through 11-09-2006",
}