A growing body of evidence indicates that circular RNAs (circRNAs) play a pivotal role in various biological processes and have a close association with the initiation and progression of diseases. Moreover, circRNAs are considered as promising biomarkers for disease diagnosis owing to their characteristics of conservation, stability and universality. Inferring disease-circRNA relationships will contribute to the understanding of disease pathology. However, it is costly and laborious to discover novel disease-circRNA interactions by wet-lab experiments, and few computational methods have been devoted to predicting potential circRNAs for diseases. Here, we advance a computational method (NCPCDA) to identify novel circRNAdisease associations based on network consistency projection. For starters, we make use of multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity, to construct the integrated circRNA similarity and disease similarity. Then, we project circRNA space and disease space on the circRNA-disease interaction network, respectively. Finally, we can obtain the predicted circRNA-disease association score matrix by combining the above two space projection scores. Simulation results show that NCPCDA can efficiently infer disease-circRNA relationships with high accuracy, obtaining AUCs of 0.9541 and 0.9201 in leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, case studies also suggest that NCPCDA is promising for discovering new disease-circRNA interactions. The NCPCDA dataset and code, as well as the detailed readme file for our code, can be downloaded from Github (https://github.com/ghli16/NNCPCD).
Benefiting from its simplicity and efficiency, particle swarm optimization (PSO) algorithm has shown great performance on various problems. However, for different optimization problems or different search areas, it is still difficult to achieve a satisfying trade-off between exploration and exploitation. On the basis of canonical PSO algorithm, a variety of improved algorithms have been proposed, which have different capabilities of exploitation and exploration, and each of them performs effective in some problems. This paper proposes a particle swarm optimization with multiple adaptive sub-swarms (PSOMAS). It uses multiple subswarms strategy, in which each sub-swarm is evolved by different algorithms, and an adaptive strategy is also used to reduce the consumption of computing resources. A comprehensive experimental study is conducted on 30 benchmark functions, to compare with several well-known variants of PSO algorithms. The results show that PSOMAS with RT=100 could obtain a better overall performance than all others. Moreover, PSOMAS could find high-quality solution in different problems by varying the value of RT.
Abstract. The water and sediment discharge regulation (WSDR) project of the Yellow River is yearly implemented from 2002, while there are few reports about its influence on zooplankton. In this paper, we studied the species constitutions, horizontal distribution and community diversity of zooplankton based on the samples collected at 18 sampling stations in the Yellow River estuary in 2014, to compare zooplankton alterations before and after the WSDR project. Phytoplankton, swimming animal and environment variables were investigated simultaneously. RELATE modules of BIOENV in software Primer 6.0 were used to seek the best matches of environmental factors that affect distribution of zooplankton. The results showed that: Species of zooplankton increased while the quantity reduced during WSDR project due to the large reduction in the number of Noctiluca miliaris. Zooplankton community structure became more stable. According to the results of BIOENV analysis, phytoplankton abundance, water depth and inorganic salts had stronger effects on the horizontal distributions of zooplankton.
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