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As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Identification of functional modules from such networks is crucial for understanding principles of cellular organization and functions. However, protein interaction data produced by high-throughput experiments are generally associated with high false positives, which makes...
For the low recognition degree of essential protein based on topological parameter, the correlation between the essentiality of proteins and their main topological parameters is analyzed and the nature of the essentiality-judgment ability of the parameters is explored firstly. Then, the mutual information of essential node these parameters contain is brought out in virtue of their correlation, based...
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 individual haplotyping problem MEC is a computational problem that, given a set of DNA sequence fragment data of an individual, induces the corresponding haplotypes by changing the smallest number of SNPs. MEC problem is NP-hard and there has been no practical exact algorithm to solve the problem. In DNA sequencing experiments, due to technical limits, the maximum length of a fragment sequenced...
The datasets identified by large-scale, high- throughput methods typically suffer from a relatively high level of noise. Combining the distribution characteristics of noise data and topological properties in the protein interaction network, we described a novel method to improve the reliability of those datasets by predicting missed interactions. The main idea of the method is to predict the interactions...
As advanced in the technologies of predicting protein-protein interactions, huge data sets portrayed as networks have been generated. Identification of functional modules from such networks is crucial for understanding principles of cellular organization and functions. In this paper, we presented a new fast agglomerate algorithm of identifying functional modules based on the edge clustering coefficients,...
In this paper, a practical algorithm PS-MEC is presented based on the idea of generating a small set of optimal results to reduce the probability of losing the best result. We design a kind of short particle code for the algorithm by taking advantage of the low heterozygous frequency of single nucleotide polymorphisms. Experimental results indicate PS-MEC can get a set containing no more than four...
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