Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A Time Series Analysis of Microarray Data
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Mining coherent gene clusters from gene-sample-time microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering in Gene Expression Data by Tendency
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A Time-Series Biclustering Algorithm for Revealing Co-Regulated Genes
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
| Hi-index | 0.00 |
In this paper, we propose a novel model, namely g-Cluster, to mine biologically significant co-regulated gene clusters. The proposed model can (1) discover extra co-expressed genes that cannot be found by current pattern/tendency-based methods, and (2) discover inverted relationship overlooked by pattern/tendency-based methods. We also design two tree-based algorithms to mine all qualified g-Clusters. The experimental results show: (1) our approaches are effective and efficient, and (2) our approaches can find an amount of co-regulated gene clusters missed by previous models, which are potentially of high biological significance