CAJ | 학술논문

双聚类是微阵列基因表达数据分析中很实用的一种数据挖掘技术，它是一种同时对微阵列基因和条件进行聚类的方法，用来挖掘基因子集在条件子集下所体现出来的生物模式。传统的双聚类算法对于庞大的基因表达数据处理效率很弱，考虑在jMetal平台上实现基因表达数据的双聚类的一种新的研究方法及思路。同时考虑加入并行策略，提高算法的效率。在酵母啤酒细胞基因表达集和人类B-细胞两个标准数据集上对两个算法进行实验验证，表明所提出算法比其他多目标双聚类算法呈现出更好的优越性。
쌍취류시미진렬기인표체수거분석중흔실용적일충수거알굴기술，타시일충동시대미진렬기인화조건진행취류적방법，용래알굴기인자집재조건자집하소체현출래적생물모식。전통적쌍취류산법대우방대적기인표체수거처리효솔흔약，고필재jMetal평태상실현기인표체수거적쌍취류적일충신적연구방법급사로。동시고필가입병행책략，제고산법적효솔。재효모비주세포기인표체집화인류B-세포량개표준수거집상대량개산법진행실험험증，표명소제출산법비기타다목표쌍취류산법정현출경호적우월성。
〔Abstract〕Biclustering is a very practical data mining technique in microarray gene expression data analysis and it is a way to cluster both microarray genes and conditions simultaneously, which is used to excavate the biological mode reflected by the gene subset set under the condition subset. The processing efficiency of traditional biclustering algorithm for large gene expression data is low, so this paper explores a new research method and idea, i.e. applying gene expression data biclustering on jMetal platform. Also the parallel strategy is proposed to improve the efficiency of the algorithm. Experiments on two datasets, yeast cell dataset and human B-cell dataset, show that our approach exhibits better and more stable performance than other multi-objective biclustering algorithms.