| Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data | |
| Huang, Qinghua1; Tao, Dacheng2; Li, Xuelong3; Liew, Alan Wee-Chung4 | |
| 作者部门 | 光学影像分析与学习中心 |
| 2012-03-01 | |
| 发表期刊 | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
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| ISSN | 1545-5963 |
| 卷号 | 9期号:2页码:560-570 |
| 产权排序 | 3 |
| 摘要 | The analysis of gene expression data obtained from microarray experiments is important for discovering the biological process of genes. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of experimental conditions. In this paper, we propose a new biclustering algorithm based on evolutionary learning. By converting the biclustering problem into a common clustering problem, the algorithm can be applied in a search space constructed by the conditions. To further reduce the size of the search space, we randomly separate the full conditions into a number of condition subsets (subspaces), each of which has a smaller number of conditions. The algorithm is applied to each subspace and is able to discover bicluster seeds within a limited computing time. Finally, an expanding and merging procedure is employed to combine the bicluster seeds into larger biclusters according to a homogeneity criterion. We test the performance of the proposed algorithm using synthetic and real microarray data sets. Compared with several previously developed biclustering algorithms, our algorithm demonstrates a significant improvement in discovering additive biclusters. |
| 文章类型 | Article |
| 关键词 | Biclustering Genetic Learning Subdimensional Search Strategy Gene Expression Data Analysis |
| 学科领域 | Biochemical Research Methods |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine ; Technology ; Physical Sciences |
| DOI | 10.1109/TCBB.2011.53 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | MICROARRAY DATA-ANALYSIS ; CLUSTERING ANALYSIS ; SET |
| 语种 | 英语 |
| WOS研究方向 | Biochemistry & Molecular Biology ; Computer Science ; Mathematics |
| WOS类目 | Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability |
| WOS记录号 | WOS:000299560500021 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/20251 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Guangdong, Peoples R China 2.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 4.Griffith Univ, Sch Informat & Commun Technol, Gold Coast, Qld 4222, Australia |
| 推荐引用方式 GB/T 7714 | Huang, Qinghua,Tao, Dacheng,Li, Xuelong,et al. Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2012,9(2):560-570. |
| APA | Huang, Qinghua,Tao, Dacheng,Li, Xuelong,&Liew, Alan Wee-Chung.(2012).Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,9(2),560-570. |
| MLA | Huang, Qinghua,et al."Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 9.2(2012):560-570. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Parallelized Evoluti（1355KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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