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Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct...
Identifying functional modules or pathways in biological networks may help better understand the underlying cellular functional mechanisms. Different from traditional network module identification problems, biological modules often overlap with each other. However, existing functional module identification algorithms can only generate overlapping densely connected modules but ignore the modules within...
To identify biologically meaningful modules in large-scale biological networks, general blockmodel network clustering algorithms have recently attracted much attention to search for groups of molecules that have similar interaction patterns in networks. However, existing blockmodel module identification algorithms suffer from the problems of prohibitive computational complexity and being trapped at...
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