Name
Title | ||
|---|---|---|
A Novel Clustering Method for Analysis of Biological Networks using Maximal Components of Graphs |
Abstract | ||
|---|---|---|
This paper proposes a novel clustering method for analyzing biological networks. In this method, each biological network is treated as an undirected graph and edges are weighted based on similarities of nodes. Then, maximal components, which are defined based on edge connectivity, are computed and the nodes are partitioned into clusters by selecting disjoint maximal components. The proposed method was applied to clustering of protein sequences and was compared with conventional clustering methods. The obtained clusters were evaluated using P-values for GO (GeneOntology) terms. The average P-values for the proposed method were better than those for other methods. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1142/9781860947995_0028 | Series on Advances in Bioinformatics and Computational Biology |
Keywords | Field | DocType |
protein sequence,maximal component,clustering,biological network | Graph,Cluster (physics),Disjoint sets,Correlation clustering,Biology,Biological network,Hierarchical clustering of networks,Consensus clustering,Artificial intelligence,Bioinformatics,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
5 | 1751-6404 | 1 |
PageRank | References | Authors |
0.41 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Morihiro Hayashida | 1 | 154 | 21.88 |
| Tatsuya Akutsu | 2 | 2169 | 216.05 |
| Hiroshi Nagamochi | 3 | 1513 | 174.40 |