A graph-theoretic technique for classification of normal and tumor tissues using gene expression microarray data.
Microarray is a very powerful and popular technology nowadays providing us with accurate predictions of the state of biological tissue samples simply based on the expression levels of genes available from it. Of particular interest in the use of microarray technology is the classification of normal and tumor tissues which is crucial for accurate diagnosis of the disease of interest. In this paper, we propose a graph-theoretic approach to the classification of normal and tumor tissues through the use of geometric representation of the graph derived from the microarray data. The accuracy of our geometric representation-based classification algorithm is shown to be comparable to that of currently known best classification algorithms for the microarray data, and in particular, the presented algorithm is shown to have the highest classification accuracy when the number of genes used for classification is small.
Kim
Saejoon
S
Deparment of Computer Science, Sogang University, Seoul, Korea. saejoon@sogang.ac.kr
D000465:Algorithms; D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012680:Sensitivity and Specificity