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Protein complex detection from protein–protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes,...
Cluster analysis is a widely used data mining technique for extracting biological knowledge from gene expression data. In this paper, we modified one of the graph-theoretic approach CAST by using fuzzy graph concept. Our algorithm FGBCAST (Fuzzy Graph Based Cluster Affinity Search Technique) is tested over three real life datasets Yeast Cell Cycle, Yeast Sporulation and Escheria Coli. The performance...
Clustering is often one of the first steps in Gene Expression Analysis. In this paper we propose a modified-QT clustering algorithm for gene expression datasets that uses a modified Pearson's correlation measure to identify the clusters in gene expression data. Experimental results show the efficiency of the proposed method over several real-life datasets. The proposed method has been found to be...
In this paper, we propose an algorithm for efficient clustering of gene expression data. The algorithm uses the concept of common neighbors and uses a fuzzy approach for detecting intersecting and overlapping clusters. We have also compared the algorithm to the existing popular approaches and found our algorithm to give good results in terms of z-score measure of cluster validity and p-value measure.
This paper presents an effective clustering method which can detect embedded and nested clusters over variable density space. The proposed method, VDSC uses a density based approach for detecting clusters of arbitrary shapes, sizes and densities. VDSC was compared with several other comparable algorithms and the experimental results show that our method could detect all clusters effectively.
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