Gene Markers Identification Algorithm for Detecting Colon Cancer Patients
XIE Juanying1, FAN Wen2
1.School of Computer Science, Shaanxi Normal University, Xi′an 7101193 2.School of Software Engineering, University of Science and Technology of China, Suzhou 215123
Abstract：To detect those few informative genes with strong classification information and identify colon cancer patients as correctly as possible, an algorithm is proposed in this paper to identify the gene markers for detecting colon cancer patients. The densities and distances are defined for genes firstly. All genes are scattered in a 2D space with gene density and distance as X-axis and Y-axis, respectively. Those genes at high density peaks are selected to construct the optimal gene subset. Then, those samples only with genes in the optimal gene subset of colon dataset are clustered by DP_K-medoids clustering algorithm. The distances between genes or samples are calculated via Euclidean distance, Manhattan distance, Chebyshev distance and the cosine distance, respectively. The experimental results demonstrate that the proposed algorithm can find the optimal gene subset of colon cancer with high accuracy, sensitivity, specificity and MCC, and with a very few number of genes as well.
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2017, Vol. 30 
