• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.767-776
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• 2008

Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.

• Journal of Yeungnam Medical Science
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• v.24 no.1
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• pp.24-40
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• 2007

Background : Accumulating evidence shows that interleukin(IL)-1 plays a critical role in inflammation and connective tissue destruction observed in both osteoarthritis and rheumatoid arthritis. IL-1 induces gene expression related to cytokines, chemokines and matrix metalloproteinases by activation of many different transcription factors. Materials and Methods : The chondrosarcoma cell line, SW1353, is known to be a valuable in vitro system for investigating catabolic gene regulation by IL-$1{\beta}$ in chondrocytic cells. To explore and analyze the changes in gene expression by IL-1 responsible for arthritis, SW1353 was treated with IL-1 for 1, 6 and 24 h and then total RNAs were purified for each time. The changes in gene expression were analyzed with 17k human cDNA microarrays and validated by semi-quantitative RT-PCR. Results : Greater than a two-fold change was observed in 1,200 genes including metallothioneins, matrix metalloproteinases, extracellular matrix proteins, antioxidant proteins, cytoskeleton proteins, cell cycle regulatory proteins, proteins for cell growth and apoptosis, signaling proteins and transcription factors. These changes appeared to be correlate with the pathophysiological changes observed in early osteoarthritis. Conclusion : cDNA microarray analysis revealed a marked variability in gene expression, and provided insight into the overall molecular changes. The result of this study provide initial information for further studies to identify therapeutic targets in osteoarthritis pathogenesis.