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Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of proteins can only be annotated computationally. Under new conditions or stimuli, not only the number and location of proteins would be changed, but also their interactions. This dynamic feature of protein interactions, however, was not considered in the existing function...
The identification of disease genes is the first step towards the understanding of genetic disease mechanisms. Although many computational algorithms are proposed to identify disease genes, they either have poor performance in terms of AUC scores or are very time consuming. To overcome these two problems, a logistic regression based algorithm is proposed in this study for identifying disease genes...
Now multiple types of data are available for prioritizing human disease genes, including gene-disease associations, disease phenotype similarities, locations of genes or their corresponding proteins in biological networks, etc. Integrating multiple types of data is expected to be effective for prioritizing human disease genes. In this paper, we propose a multiple data integration method based on the...
Various algorithms have been proposed to identify protein complexes from PPI networks, based on the assumption that protein complexes are densely connected subgraphs. In this study, we conclude that most known protein complexes do not exhibit dense structures in S. cerevisiae PPI network, but maintain starlike structures in the network. Moreover, vertices of protein complexes are not sparsely connected...
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