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The identification of essential proteins in the protein- protein interaction (PPJ) network tends to only focus on the topological characteristics of t he nodes, and the PPI data contains high false positive, the neighborhood information of nodes and the influence of complex mining on the recognition of essential proteins are not considered comprehensively by the essential proteins recognition algorithm based on complex information, so the accuracy and specificity of the recognition results are not high. In order to deal with these problems, an essential proteins prediction algorithm based on participation degree in protein complex and density (PEC) is proposed. Firstly, the GO annotation information and the edge aggregation coefficient are used to construct the weighted PPI network to overcome the influence of false positives on the experimental results. Based on the edge weight of protein interaction, the similarity matrix is constructed. The maximum difference between eigenvectors is designed to automatically determine the partition number K. Meanwhile, K initial clustering centers are selected according to the degree of protein nodes in the weighted network. Furthermore, the spectral clustering and the fuzzy Cmeans (FCM) clustering algorithm are combined to excavate the protein complex, thus improving the clustering accuracy and reduces the data dimension. Secondly, based on the degree of participation in protein complex and the neighborhood subgraph density, the scores of the essential proteins are proposed. The experiment results on DIP and Krogan datasets show that, compared with 10 classic algorithms such as DC, BC, CC, SC, IC, PeC, WDC, LIDC, LBCC and UC, PEC can correctly identify more essential proteins with higher accuracy and specificity. [ABSTRACT FROM AUTHOR]

The uncertain factor rainfall is hard to accurately handle and the ant colony clustering algorithm is easy to get caught into sub-optimal solution and the searching speed is low in searching space. In order to improve the prediction accuracy of landslide hazard, we propose an uncertain ant colony clustering algorithm based on approximate backbone. Firstly, it utilizes the Gauss point probability model to describe the uncertain data and measure their similarity. Secondly, we introduce the pheromone redistribution and adaptive dynamic variables to update the local pheromone and global pheromone for improving the algorithm's searching speed, and load the genetic algorithm to prevent it from falling into local optimum early. Finally, combining the approximate backbone theory, we build an uncertain ant colony clustering algorithm model based on approximate backbone, which reduces the iteration times and obtains the clustering solution rapidly. Experiments on UCI true datascts and landslide experiment data-sets of the Baota district of Yan'an show that the proposed method achieves a higher clustering quality and the prediction accuracy reaches 93. 3%, which verifies its feasibility. [ABSTRACT FROM AUTHOR]