Finding edging genes from microarray data.
BACKGROUND
A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, we propose an algorithm to effectively find these EGs.


RESULTS
We tested our algorithm with five microarray datasets. The results are compared with the border-based algorithm which was used to find gene groups and subsequently divide different classes of tissues. Our algorithm finds a significantly larger amount of EGs than does the border-based algorithm. As our algorithm prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm.


BACKGROUND
The algorithm proposed is implemented in C++ on Linux platform. The EGs in five microarray datasets are calculated. The preprocessed datasets and the discovered EGs are available at http://www3.it.deakin.edu.au/~phoebe/microarray.html.
An
Jiyuan
J
Faculty of Science and Technology, School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, Victoria 3125, Australia. jiyuan.an@gmail.com
Chen
Yi-Ping Phoebe
YP
D000465:Algorithms; D030541:Databases, Genetic; D019277:Entropy; D015972:Gene Expression Regulation, Neoplastic; D005796:Genes; D052138:Genes, Neoplasm; D006801:Humans; D020407:Internet; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software