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With the progress of studies and researches on the biological networks, plenty of excellent clustering algorithms have been proposed. Nevertheless, not only different algorithms but also the same algorithms with different characteristics result in different performances on the same biological networks. Therefore, it might be difficult for researchers to choose an appropriate clustering algorithm to...
In order to reduce channel contention, support scalability and prolong the lifetime of the sensor networks, sensor nodes are often grouped into clusters. Algorithm for Cluster Establishment (ACE) is a clustering algorithm for sensor networks that uses three rounds of feedback to induce the formation of a highly efficient cover of uniform clusters over the network. In this paper, we present an optimizing...
As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Several graph clustering approaches have been proposed to detect functional modules from such networks. However, all methods of predicting protein interactions are known to yield a nonnegligible amount of false positives. Most of the graph clustering algorithms are challenging...
The concept of structural clusters defining the vocabulary of protein structure is one of the central concepts in the modern theory of protein folding. Typically clusters are found by a variation of the K-means or K-NN algorithm. In this paper we study approaches to estimating the number of clusters in data. The optimal number of clusters is believed to result in a reliable clustering. Stability with...
Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes and functional modules, which is crucial to understanding the principles of cellular organization and prediction of protein functions. In the past few years, many computational methods have been proposed. However, it is always a challenging task to evaluate how well the clusters...
The problem of discovering motifs from protein sequences is a critical and challenging task in the field of bioinformatics. The task involves clustering relatively similar protein segments from a huge collection of protein sequences and culling high quality motifs from a set of clusters. A granular computing strategy combined with K-means clustering algorithm was previously proposed for the task,...
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