Name
Abstract | ||
|---|---|---|
This paper introduces an enhanced version of Pearson’s correlation coefficient (PCC) to achieve better biclustering-enabled co-expression analysis. The modified measure called local pearson correlation measure (LPCM) helps detect shifting, scaling, and shifting-and-scaling correlation patterns effectively over gene expression data in the presence of outlier. An LPCM-based biclustering technique called local correlation-based biclustering technique (LCBT) has also been proposed to identify biclusters of high biological significance. The biclustering results have been established both statistically and biologically using benchmarked gene expression data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s13721-019-0211-7 | Network Modeling Analysis in Health Informatics and Bioinformatics |
Keywords | DocType | Volume |
Microarray data, Clustering, Biclustering, P value, GO annotation, Proximity measure | Journal | 9 |
Issue | ISSN | Citations |
1 | 2192-6662 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pallabi Patowary | 1 | 0 | 0.34 |
| Rosy Sarmah | 2 | 6 | 2.80 |
| Dhruba K. Bhattacharyya | 3 | 226 | 27.72 |