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Physical interactions between the proteins in a living organism helps in identification of most protein-protein interaction data. The annotated proteins are previously known by their functions. Their knowledge is definite. The un-annotated proteins are annotated based on estimation of such similar functions. Generally a cluster containing annotated nodes with their adjacent unlabeled nodes is assumed...
New technological advances in large-scale protein-protein interaction (PPI) detection provide researchers a valuable source for elucidating the bimolecular mechanism in the cell. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. Many people try to solve...
As more and more high-throughput protein-protein interactions data are collected, a large fraction of newly discovered proteins have an unknown functional role. A challenge to the scientific community is to assign these newly proteins with a biological function that can be verified by experiment. On the basis of thorough analysis of existing protein function prediction, we take double direction enumeration...
Dense subgraphs of protein interaction networks are believed to be potential protein complexes and play an important role in analyzing cellular organization and predicting functions of proteins. In this paper, we present a new algorithm LD-Miner for mining l-dense subgraphs in protein interaction networks. We apply algorithm LD-Miner to the protein interaction network of Saccharomyces cerevisiae collected...
The essentiality of a gene or protein is important for understanding the minimal requirements for cellular survival and development. Numerous computational methodologies have been proposed to detect essential proteins from large protein-protein interactions (PPI) datasets. However, only a handful of overlapping essential proteins exists between them. This suggests that the methods may be complementary...
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