Finding edging genes from microarray data.

@article{citeulike:2965210, abstract = {MOTIVATION: 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. RESULT: 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. AVAILABILITY: 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/ approximately phoebe/microarray.html.}, address = {Faculty of Science and Technology, Deakin University, Australia.}, author = {An, J. and Chen, Y. P. }, citeulike-article-id = {2965210}, doi = {http://dx.doi.org/10.1016/j.jbiotec.2008.04.004}, issn = {0168-1656}, journal = {Journal of biotechnology}, keywords = {microarray}, month = {June}, number = {3}, pages = {233--240}, posted-at = {2008-07-05 01:16:04}, priority = {2}, title = {Finding edging genes from microarray data.}, url = {http://dx.doi.org/10.1016/j.jbiotec.2008.04.004}, volume = {135}, year = {2008} }

TY - JOUR ID - citeulike:2965210 L3 - citeulike-article-id:2965210 TI - Finding edging genes from microarray data. JF - Journal of biotechnology VL - 135 IS - 3 SP - 233 EP - 240 AD - Faculty of Science and Technology, Deakin University, Australia. SN - 0168-1656 N2 - MOTIVATION: 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. RESULT: 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. AVAILABILITY: 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/ approximately phoebe/microarray.html. KW - microarray AU - An, J AU - Chen, YP PY - 2008/06/30/ UR - http://dx.doi.org/10.1016/j.jbiotec.2008.04.004 ER -