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Traditional methods for microarray datasets analysis often find the co-expression genes. However, these methods may miss the genes which are differential co-expression patters under different datasets. Mining these differential co-expression patterns is more valuable for inferring regulator. In this paper, we develop an algorithm, MSPattern, to mine maximal subspace differential co-expression patterns...
Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, we propose an algorithm, FDCluster, to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses...
In this paper, we propose a novel method to measure the semantic similarity between genes. The key principle of our method relies on both path length between genes' annotation terms in the Gene Ontology and depth of their annotation terms' common ancestor node in the Gene Ontology. Our method applies an exponential transfer function which includes path length and depth as its two parameters to get...
The prediction of protein function is one of the most challenging problems in bioinformatics. Several studies have shown that the prediction using PPI is promising. However, the PPI data generated from high-throughput experiments are very noisy, which renders great challenges to the existing methods. In this paper, we propose an algorithm, MFC, to efficiently mine maximal frequent dense subgraphs...
The recent development of high-throughout techniques to generate large volumes of protein-protein interaction(PPI) data, which increased the need for methods that annotate the function of protein. Some methods use indirect method to predict proteins function. However, due to the nature of noise, the relationship between proteins may not be existed in truth. In this paper, we propose a method of protein...
The prediction of protein function is one of the problems arising in the recent progress in bioinformatics. A common used approach is to derive clusters from PPI dataset. However, such results often contain false positives. In this study, we propose a novel algorithm, EVDENSE, to efficiently mine frequent dense subgraphs from PPI networks. Instead of using summary graph, EVDENSE produces frequent...
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