.png){kind=icon}
返回
Semi-supervised consensus clustering for gene expression data analysis
.png){kind=icon}
OA
AI
DOI：10.1186/1756-0381-7-7.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and domain knowledge. Methods: We proposed semi-supervised consensus clustering (SSCC) to integrate the consensus clustering with semi-supervised clustering for analyzing gene expression data. We investigated the roles of consensus clustering and prior knowledge in improving the quality of clustering. SSCC was compared with one semi-supervised clustering algorithm, one consensus clustering algorithm, and k-means. Experiments on eight gene expression datasets were performed using h-fold cross-validation. Results: Using prior knowledge improved the clustering quality by reducing the impact of noise and high dimensionality in microarray data. Integration of consensus clustering with semi-supervised clustering improved performance as compared to using consensus clustering or semi-supervised clustering separately. Our SSCC method outperformed the others tested in this paper.
Keyword:
Semi-supervised clustering
Consensus clustering
Semi-supervised consensus clustering
Gene expression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.1
论文数:
700
被引数:
1.5K
机构
引用论文
Tree rows in temperate agroforestry croplands alter the composition of soil bacterial communities.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Long-term alterations of ERP systems: lasting through relinquishment and regenerationERP系统的长期调整：贯穿放弃与再生
PREAI
分享收藏Numerical 3-D Simulation of a Cathodic Protection System for a Buried Pipe Segment Surrounded by a Load Relieving U-Shaped VaultCORROSION
CORROSIONIF0
PREAI
分享收藏MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemiaNATURE GENETICS
NATURE GENETICSIF31.8
PREAI
分享收藏
查看更多内容