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Hwang, Soyoun, Taekeon Lee, and Youngmi Yoon. "Exploring disease comorbidity in a module–module interaction network." Journal of Bioinformatics and Computational Biology 18, no. 02 (April 2020): 2050010. http://dx.doi.org/10.1142/s0219720020500109.
Повний текст джерелаАнотація:
Understanding disease comorbidity contributes to improved quality of life in patients who are suffering from multiple diseases. Therefore, to better explore comorbid diseases, the clarification of associations between diseases based on biological functions is essential. In our study, we propose a method for identifying disease comorbidity in a module-based network, named the module–module interaction (MMI) network, which represents how biological functions influence each other. To construct the MMI network, we detected gene modules — sets of genes that have a higher probability of taking part in specific functions — and established a link between these modules. Subsequently, we constructed disease-related networks in the MMI network to understand inherent disease mechanisms and calculated comorbidity scores of disease pairs using Gene Ontology (GO) terms. Our results show that we can obtain further information on disease mechanisms by considering interactions between functional modules instead of between genes. In addition, we verified that predicted comorbid relationships of disease pairs based on the MMI network are more significant than those based on the protein–protein interaction (PPI) network. This study can be useful to elucidate the mechanisms underlying comorbidities for further study, which will provide a broader insight into the pathogenesis of diseases.
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He, Li, Xian-Xu Song, Mei Wang, and Ben-Zhuo Zhang. "Screening feature modules and pathways in glioma using EgoNet." Open Life Sciences 12, no. 1 (October 23, 2017): 277–84. http://dx.doi.org/10.1515/biol-2017-0032.
Повний текст джерелаАнотація:
AbstractBackgroundTo investigate differential egonetwork modules and pathways in glioma using EgoNet algorithm.MethodologyBased on microarray data, EgoNet algorithm mainly comprised three stages: construction of differential co-expression network (DCN); EgoNet algorithm used to identify candidate ego-network modules based on the increased classification accuracy; statistical significance for candidate modules using random permutation testing. After that, pathway enrichment analysis for differential ego-network modules was implemented to illuminate the biological processes.ResultsWe obtained 109 ego genes. From every ego gene, we progressively grew the ego-networks by levels; we extracted 109 ego-networks and the mean node size in an ego-network was 6. By setting the classification accuracy threshold at 0.90 and the count of nodes in an ego-network module at 10, we extracted 8 candidate ego-network modules. After random permutation test with 1000 times, 5 modules including module 59, 72, 78, 86, and 90 were identified to be significant. Of note, the genes of module 90 and 86 were enriched in the pathway of resolution of sister chromatid cohesion and mitotic prometaphase, respectively.ConclusionThe identified modules and their corresponding ego genes might be beneficial in revealing the pathology underlying glioma and give insight for future research of glioma.
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Qiao, Hu, Zhaohui Xu, Jiang He, and Ying Xiang. "Product Module Network Modeling and Evolution Analysis." Computational Intelligence and Neuroscience 2019 (March 6, 2019): 1–8. http://dx.doi.org/10.1155/2019/2186916.
Повний текст джерелаАнотація:
Modular technology for product design and manufacturing is an effective way to solve mass customization problems. One difficulty in the application of modular technology is that the characteristics of mass customization, such as multi batch and small batch, easily increase the complexity of the module structure of the enterprise products. To address this problem, based on complex network theory, the enterprise products module is mapped as the vertex of the network, the number of modules used is mapped as the node weight, the dependency between the modules is mapped to the edge, and the product module network is established. The brittleness risk entropy of the product module network is put forward by considering the internal and external factors that influence the application of the enterprise module to determine the rationality of the required modules' organizational structures. Then, the stability uncertainty of the product module network can be determined by calculating the brittleness risk entropy, in which the subsystem that is the most brittle risk entropy can be identified. And the evolution of the product module network can be promoted by changing factors of the entropy maximum subsystem. To analyze the change in the product module network caused by module evolution, a BBV (Barrat–Barthelemy–Vespignani) model of the product module network is established to dynamically determine the brittle risk of the product module network. Finally, the modularity structure of a series of special vehicles is used as an example to verify the presented method, and the results confirm the rationality and effectiveness of the method.
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Wu, Wei, Guangmin Hu, and Fucai Yu. "An Unsupervised Learning Method for Attributed Network Based on Non-Euclidean Geometry." Symmetry 13, no. 5 (May 19, 2021): 905. http://dx.doi.org/10.3390/sym13050905.
Повний текст джерелаАнотація:
Many real-world networks can be modeled as attributed networks, where nodes are affiliated with attributes. When we implement attributed network embedding, we need to face two types of heterogeneous information, namely, structural information and attribute information. The structural information of undirected networks is usually expressed as a symmetric adjacency matrix. Network embedding learning is to utilize the above information to learn the vector representations of nodes in the network. How to integrate these two types of heterogeneous information to improve the performance of network embedding is a challenge. Most of the current approaches embed the networks in Euclidean spaces, but the networks themselves are non-Euclidean. As a consequence, the geometric differences between the embedded space and the underlying space of the network will affect the performance of the network embedding. According to the non-Euclidean geometry of networks, this paper proposes an attributed network embedding framework based on hyperbolic geometry and the Ricci curvature, namely, RHAE. Our method consists of two modules: (1) the first module is an autoencoder module in which each layer is provided with a network information aggregation layer based on the Ricci curvature and an embedding layer based on hyperbolic geometry; (2) the second module is a skip-gram module in which the random walk is based on the Ricci curvature. These two modules are based on non-Euclidean geometry, but they fuse the topology information and attribute information in the network from different angles. Experimental results on some benchmark datasets show that our approach outperforms the baselines.
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Zhou, Hao, Jun Ping Wang, and Suo Ju He. "Self-Adapted Admission Control Model for Parlay on Heterogeneous Network." Advanced Materials Research 546-547 (July 2012): 1164–70. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1164.
Повний текст джерелаАнотація:
Parlay Gateway has played an important role in application development on heterogeneous networks. For many third part’s applications accessing Telecommunication Network through it, it is likely to become the bottleneck of whole system. In this paper, the author proposed a self-adapted control model which is effective in admission control of Parlay Gateway. This method is made up of five modules, which are admission control module, waiting queue module, token generating module, scheduling module, and the overload detecting module. According to some simulation results, the author found it is useful and easy to be implemented.
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Zhang, Shuqin. "Hierarchical Modular Structure Identification with Its Applications in Gene Coexpression Networks." Scientific World Journal 2012 (2012): 1–8. http://dx.doi.org/10.1100/2012/523706.
Повний текст джерелаАнотація:
Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in many networks such as biological networks and social networks. Compared to the partitional module identification methods, less research is done on the inference of hierarchical modular structure. In this paper, we propose a method for constructing the hierarchical modular structure based on the stochastic block model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene coexpression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions.
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TSIOUTSIAS, DIMITRIS I., and ERIC MJOLSNESS. "OPTIMIZATION DYNAMICS FOR PARTITIONED NEURAL NETWORKS." International Journal of Neural Systems 05, no. 04 (December 1994): 275–86. http://dx.doi.org/10.1142/s0129065794000281.
Повний текст джерелаАнотація:
Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.
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Liang, Gen, Xiaoxue Guo, Guoxi Sun, and Jingcheng Fang. "A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks." Computational Intelligence and Neuroscience 2020 (November 24, 2020): 1–20. http://dx.doi.org/10.1155/2020/8828355.
Повний текст джерелаАнотація:
A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains.
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Perrin, Dimitri, and Guido Zuccon. "Recursive module extraction using Louvain and PageRank." F1000Research 7 (August 14, 2018): 1286. http://dx.doi.org/10.12688/f1000research.15845.1.
Повний текст джерелаАнотація:
Biological networks are highly modular and contain a large number of clusters, which are often associated with a specific biological function or disease. Identifying these clusters, or modules, is therefore valuable, but it is not trivial. In this article we propose a recursive method based on the Louvain algorithm for community detection and the PageRank algorithm for authoritativeness weighting in networks. PageRank is used to initialise the weights of nodes in the biological network; the Louvain algorithm with the Newman-Girvan criterion for modularity is then applied to the network to identify modules. Any identified module with more than k nodes is further processed by recursively applying PageRank and Louvain, until no module contains more than k nodes (where k is a parameter of the method, no greater than 100). This method is evaluated on a heterogeneous set of six biological networks from the Disease Module Identification DREAM Challenge. Empirical findings suggest that the method is effective in identifying a large number of significant modules, although with substantial variability across restarts of the method.
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Fu, Zeyuan. "Computer Network Intrusion Anomaly Detection with Recurrent Neural Network." Mobile Information Systems 2022 (March 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/6576023.
Повний текст джерелаАнотація:
Network intrusion anomaly detection technique has been widely employed in computer network environments as a highly effective security prevention method. As network technology and network applications have advanced at a rapid pace, so too has network data traffic, resulting in an increase in virus and attack kinds. In the face of large-scale traffic and characteristic information, traditional intrusion detection will have problems such as low detection accuracy, high false negatives, and reliance on dimensionality reduction algorithms. Therefore, it is particularly important to establish a fast and efficient network intrusion anomaly detection method to deal with the current complex network environment. This work designs a computer network intrusion detection model with a recurrent neural network in order to explore a new intrusion detection method. The main purpose of this article include the following: (1) design a network security emergency response system architecture with the recurrent neural network model. This system consists of a management center module, a knowledge database module, a data acquisition module, a risk detection tool module, a risk analysis and processing module, a data protection module, and a remote connection auxiliary module. The modules cooperate with each other to complete system functions. (2) Aiming at the risk analysis and processing module, a network intrusion detection model combining bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) is designed. In view of the lack of consideration of the before-and-after relevance of intrusion data features and the multifeature problem in existing models, the use of BiLSTM to extract the relevance between features and the use of DNN to extract deeper features are proposed. Aiming at the problem that the model lacks consideration of the importance of features, it is proposed to embed an attention mechanism into the network to increase consideration for the importance of features. (3) Massive experiments have verified the reliability and effectiveness of the method proposed in this work.
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Chen, Jinzhi, Dejun Zhang, Yiqi Wu, Yilin Chen, and Xiaohu Yan. "A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery." Remote Sensing 14, no. 9 (May 9, 2022): 2276. http://dx.doi.org/10.3390/rs14092276.
Повний текст джерелаАнотація:
The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy.
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Hou, Jie, Xiufen Ye, Chuanlong Li, and Yixing Wang. "K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks." Genes 12, no. 1 (January 12, 2021): 87. http://dx.doi.org/10.3390/genes12010087.
Повний текст джерелаАнотація:
Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance correlation for WGCNA to construct a gene co-expression network, and present a new approach called the k-module algorithm to improve the WGCNA clustering results. This method can assign all genes to the module with the highest mean connectivity with these genes. This algorithm re-adjusts the results of hierarchical clustering while retaining the advantages of the dynamic tree cut method. The validity of the algorithm is verified using six datasets from microarray and RNA-seq data. The k-module algorithm has fewer iterations, which leads to lower complexity. We verify that the gene modules obtained by the k-module algorithm have high enrichment scores and strong stability. Our method improves upon hierarchical clustering, and can be applied to general clustering algorithms based on the similarity matrix, not limited to gene co-expression network analysis.
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Wang, Hong Wei, Chun Lei Zhang, Xiao Ming Ni, Zhi Gang Gao, Wen Kai Zhang, Xiao Ni Wang, Zhi Tian Hao, and Ming Hui Wang. "Distributed Temperature Sensor Network System." Applied Mechanics and Materials 190-191 (July 2012): 968–71. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.968.
Повний текст джерелаАнотація:
The wireless sensor network (WSN) is the development trend of the technology of sensor. This paper, based on the nRF905 wireless module, introduces a wireless temperature gathering and transmitting system. From RF modules and master control module, the hardware platform has been designed, beyond that, the paper introduces the temperature sensor network software design. The test show that the system is stable, and datas are reliable.
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Liu, Rui. "A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks." Computational Intelligence and Neuroscience 2021 (December 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/4367875.
Повний текст джерелаАнотація:
In this paper, we propose a multiresidual module convolutional neural network-based method for athlete pose estimation in sports game videos. The network firstly designs an improved residual module based on the traditional residual module. Firstly, a large perceptual field residual module is designed to learn the correlation between the athlete components in the sports game video within a large perceptual field. A multiscale residual module is designed in the paper to better solve the inaccuracy of the pose estimation due to the problem of scale change of the athlete components in the sports game video. Secondly, these three residual modules are used as the building blocks of the convolutional neural network. When the resolution is high, the large perceptual field residual module and the multiscale residual module are used to capture information in a larger range as well as at each scale, and when the resolution is low, only the improved residual module is used. Finally, four multiresidual module convolutional neural networks are used to form the final multiresidual module stacked convolutional neural network. The neural network model proposed in this paper achieves high accuracy of 89.5% and 88.2% on the upper arm and lower arm, respectively, so the method in this paper reduces the influence of occlusion on the athlete’s posture estimation to a certain extent. Through the experiments, it can be seen that the proposed multiresidual module stacked convolutional neural network-based method for athlete pose estimation in sports game videos further improves the accuracy of athlete pose estimation in sports game videos.
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Tran, Tien-Dzung, and Yung-Keun Kwon. "The relationship between modularity and robustness in signalling networks." Journal of The Royal Society Interface 10, no. 88 (November 6, 2013): 20130771. http://dx.doi.org/10.1098/rsif.2013.0771.
Повний текст джерелаАнотація:
Many biological networks tend to have a high modularity structural property and the dynamic characteristic of high robustness against perturbations. However, the relationship between modularity and robustness is not well understood. To investigate this relationship, we examined real signalling networks and conducted simulations using a random Boolean network model. As a result, we first observed that the network robustness is negatively correlated with the network modularity. In particular, this negative correlation becomes more apparent as the network density becomes sparser. Even more interesting is that, the negative relationship between the network robustness and the network modularity occurs mainly because nodes in the same module with the perturbed node tend to be more sensitive to the perturbation than those in other modules. This result implies that dynamically similar nodes tend to be located in the same module of a network. To support this, we show that a pair of genes associated with the same disease or a pair of functionally similar genes is likely to belong to the same module in a human signalling network.
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Schmidt, Christoph, Diana Piper, Britta Pester, Andreas Mierau, and Herbert Witte. "Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks." International Journal of Neural Systems 28, no. 04 (March 12, 2018): 1750051. http://dx.doi.org/10.1142/s0129065717500514.
Повний текст джерелаАнотація:
Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework’s potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.
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Wang, Li, Cai, Sheu, Tsai, Wu, Li, and Hou. "Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis." Journal of Clinical Medicine 8, no. 8 (August 2, 2019): 1160. http://dx.doi.org/10.3390/jcm8081160.
Повний текст джерелаАнотація:
Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.
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Huang, Zhongzhan, Senwei Liang, Mingfu Liang, and Haizhao Yang. "DIANet: Dense-and-Implicit Attention Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4206–14. http://dx.doi.org/10.1609/aaai.v34i04.5842.
Повний текст джерелаАнотація:
Attention networks have successfully boosted the performance in various vision problems. Previous works lay emphasis on designing a new attention module and individually plug them into the networks. Our paper proposes a novel-and-simple framework that shares an attention module throughout different network layers to encourage the integration of layer-wise information and this parameter-sharing module is referred to as Dense-and-Implicit-Attention (DIA) unit. Many choices of modules can be used in the DIA unit. Since Long Short Term Memory (LSTM) has a capacity of capturing long-distance dependency, we focus on the case when the DIA unit is the modified LSTM (called DIA-LSTM). Experiments on benchmark datasets show that the DIA-LSTM unit is capable of emphasizing layer-wise feature interrelation and leads to significant improvement of image classification accuracy. We further empirically show that the DIA-LSTM has a strong regularization ability on stabilizing the training of deep networks by the experiments with the removal of skip connections (He et al. 2016a) or Batch Normalization (Ioffe and Szegedy 2015) in the whole residual network.
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Pedersen, Mangor, Amir Omidvarnia, James M. Shine, Graeme D. Jackson, and Andrew Zalesky. "Reducing the influence of intramodular connectivity in participation coefficient." Network Neuroscience 4, no. 2 (January 2020): 416–31. http://dx.doi.org/10.1162/netn_a_00127.
Повний текст джерелаАнотація:
Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coefficient ( PC) is typically used to measure this attribute, although its value also depends on the size and connectedness of the module it belongs to and may lead to nonintuitive identification of highly connected nodes. Here, we develop a normalized PC that reduces the influence of intramodular connectivity compared with the conventional PC. Using brain, C. elegans, airport, and simulated networks, we show that our measure of participation is not influenced by the size or connectedness of modules, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.
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Sun, Junwei, Juntao Han, Gaoyong Han, Yanfeng Wang, and Peng Liu. "Memristive Hopfield Neural Network for Reasoning with Incomplete Information and Its Circuit Implementation." Journal of Nanoelectronics and Optoelectronics 16, no. 9 (September 1, 2021): 1401–11. http://dx.doi.org/10.1166/jno.2021.3104.
Повний текст джерелаАнотація:
Memristor-based neural networks have been extensively studied, but reasoning as an important topic of artificial intelligence is rarely implemented directly by circuit. Reasoning, as an important part of artificial intelligence, is an open and challenging problem to be solved. In this paper, memristive hopfield neural network is designed to realize reasoning. The designed circuit consists of four modules, namely a signal processing module, an iterator module, a signal input module and a signal output module. The signal processing module performs iterative operations under the control of the iterator module, so that the output signals of memristive hopfield neural network can converge to the final states. Reasoning is one of the basic forms of thinking, and is the process of drawing result from one or several given conditions. A guessing game for athletes is completed by the designed circuit which can reason the name of the athlete from incomplete information. The simulation results verify the feasibility of the circuit for reasoning.
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Lagana, Alessandro, Ben Readhead, Deepak Perumal, Brian Kidd, Hearn Jay Cho, Ajai Chari, Sundar Jagannath, Joel Dudley, and Samir Parekh. "Towards a Network-Based Molecular Taxonomy of Newly Diagnosed Multiple Myeloma." Blood 126, no. 23 (December 3, 2015): 840. http://dx.doi.org/10.1182/blood.v126.23.840.840.
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Abstract Recent advances in computational biology have led to the development of novel and sophisticated methods to model large datasets measured from complex organisms based on integrative network biology. Networks can provide valuable insight into key biological processes and allow for a deeper understanding of the complexity of cellular systems and disease mechanisms. We developed and applied a network biology approach to infer an improved molecular model and understanding of newly diagnosed multiple myeloma (MM). We constructed the first co-expression network of MM based on RNA-seq data from the current release (IA4) of the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study dataset. The data set consists of 92 samples from newly diagnosed MM patients. Whole Exome Sequencing (WES) data available for 77 out of 92 samples allowed the integration of somatic mutations into the network. Our analysis organized 23,033 genes into 50 co-expression modules. We then evaluated the molecular activity of co-expression modules for concordance with molecular traits. We performed module enrichment analysis against Gene Ontology terms, pathways, chromosome locations, protein-protein interaction networks, MM-associated gene sets and drug-target databases. Analysis of the newly diagnosed multiple myeloma network model (MMNet) revealed known and novel molecular features of multiple myeloma. The integration of MMNet with somatic mutations data unveiled a significant association between mutation burden and the activation of several modules. Fundamental biological processes such as DNA repair, cell cycle, signal transduction, NK-kappaB cascade and MAPK signaling characterized such modules. Interestingly, a number of mutated genes demonstrated pluripotent associations with co-expression module activity. For example, FGFR3 was correlated with expression of several modules, including one enriched for RNA processing and translation-related processes and included the known MM-associated genes FRZB and CCND3. Similarly, the frequently mutated gene DIS3 was significantly associated to five different modules, including the translation-related module and a module enriched for the 1q locus. Our results have identified novel key driver genes that may inform therapy prioritization. The MMNet topology revealed a far greater molecular heterogeneity in primary MM underscoring opportunities to improve the molecular taxonomy of this disease. We identified several modules associating with previously described MM classes, including a module enriched for genes up regulated in the UAMS MS class characterized by spiked expression of WHSC1 and FGFR3. Module connectivity confirmed the central role of both genes, WHSC1 being the top hub gene, i.e. the most connected gene in the module, and FGFR3 being among the top 10 hubs. Consistent with previous findings, this module was characterized by negative correlation with aneuploidy. We found other modules enriched for genes dysregulated in other UAMS classes, such as MF, CD1 and CD2. We also identified several modules associating with relevant biological processes such as apoptosis, cell communication, Wnt and Toll-like receptor signaling. Correlation of modules expression with clinical traits identified insights into genetic subgroups of MM that are not previously described. For examples, we found a module positively correlated to the African American ethnicity. This module was also characterized by enrichment for genes in the fragile regions 5q31 and 6q21. These findings may provide important and exciting insights into the biology of MM among African Americans as they are at increased risk for MM. Our integrative network analysis of the CoMMpass dataset uncovers novel and complex patterns of genomic perturbation, key drivers and associations between clinical traits and genetic markers in newly diagnosed MM patients. Disclosures Chari: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Biotest: Other: Institutional Research Funding; Array Biopharma: Consultancy, Other: Institutional Research Funding, Research Funding; Novartis: Consultancy, Research Funding; Onyx: Consultancy, Research Funding. Jagannath:BMS: Honoraria; MERCK: Honoraria; Novartis Pharmaceuticals Corporation: Honoraria; Celgene: Honoraria; Janssen: Honoraria. Dudley:Ayasdi, Inc: Other: Equity; Personalis: Patents & Royalties; NuMedii, Inc: Patents & Royalties; GlaxoSmithKline: Consultancy; Janssen Pharmaceuticals: Consultancy; Ecoeos, Inc: Other: Equity.
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MA, SHUANGGE, YUAN HUANG, JIAN HUANG, and KUANGNAN FANG. "Gene network-based cancer prognosis analysis with sparse boosting." Genetics Research 94, no. 4 (August 2012): 205–21. http://dx.doi.org/10.1017/s0016672312000419.
Повний текст джерелаАнотація:
SummaryHigh-throughput gene profiling studies have been extensively conducted, searching for markers associated with cancer development and progression. In this study, we analyse cancer prognosis studies with right censored survival responses. With gene expression data, we adopt the weighted gene co-expression network analysis (WGCNA) to describe the interplay among genes. In network analysis, nodes represent genes. There are subsets of nodes, called modules, which are tightly connected to each other. Genes within the same modules tend to have co-regulated biological functions. For cancer prognosis data with gene expression measurements, our goal is to identify cancer markers, while properly accounting for the network module structure. A two-step sparse boosting approach, called Network Sparse Boosting (NSBoost), is proposed for marker selection. In the first step, for each module separately, we use a sparse boosting approach for within-module marker selection and construct module-level ‘super markers’. In the second step, we use the super markers to represent the effects of all genes within the same modules and conduct module-level selection using a sparse boosting approach. Simulation study shows that NSBoost can more accurately identify cancer-associated genes and modules than alternatives. In the analysis of breast cancer and lymphoma prognosis studies, NSBoost identifies genes with important biological implications. It outperforms alternatives including the boosting and penalization approaches by identifying a smaller number of genes/modules and/or having better prediction performance.
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Miecznikowski, Jeffrey C., Daniel P. Gaile, Xiwei Chen, and David L. Tritchler. "Identification of consistent functional genetic modules." Statistical Applications in Genetics and Molecular Biology 15, no. 1 (January 1, 2016): 1–18. http://dx.doi.org/10.1515/sagmb-2015-0026.
Повний текст джерелаАнотація:
AbstractIt is often of scientific interest to find a set of genes that may represent an independent functional module or network, such as a functional gene expression module causing a biological response, a transcription regulatory network, or a constellation of mutations jointly causing a disease. In this paper we are specifically interested in identifying modules that control a particular outcome variable such as a disease biomarker. We discuss the statistical properties that functional networks should possess and introduce the concept of network consistency which should be satisfied by real functional networks of cooperating genes, and directly use the concept in the pathway discovery method we present. Our method gives superior performance for all but the simplest functional networks.
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Wang, Hong Wei, Xu Tian, Gai Hong Du, Wen Kai Zhang, Yang Men Tian, Qimu Su Rong, and Xiao Ni Wang. "Wireless Sensor Network Platform Based on STM32." Advanced Materials Research 787 (September 2013): 1011–16. http://dx.doi.org/10.4028/www.scientific.net/amr.787.1011.
Повний текст джерелаАнотація:
This paper, based on STM32 and nRF905 wireless module, introduces a wireless temperature gathering and transmitting system. From RF modules and master control module, the hardware platform has been designed, beyond that, the paper introduces the temperature sensor network software design. The test show that the system is stable, and datas are reliable.
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Chen, He-Gang, and Xiong-Hui Zhou. "MNBDR: A Module Network Based Method for Drug Repositioning." Genes 12, no. 1 (December 27, 2020): 25. http://dx.doi.org/10.3390/genes12010025.
Повний текст джерелаАнотація:
Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.
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Cui, Ze-Jia, Xiong-Hui Zhou, and Hong-Yu Zhang. "DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer." Genes 10, no. 8 (July 28, 2019): 571. http://dx.doi.org/10.3390/genes10080571.
Повний текст джерелаАнотація:
Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.
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Zeng, Xiao Hui, Man Hua Li, and Wen Lang Luo. "Research on a Remote Network Monitoring Model for Large-Scale Materials Manufacturing." Key Engineering Materials 474-476 (April 2011): 1999–2003. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1999.
Повний текст джерелаАнотація:
A remote network monitoring model for large-scale materials manufacturing is proposed, including five modules: center control module, data collection and fault alarm module, graph drawing module and data storage module. The center control module not only interacts with users, but also controls the other four modules to work together in harmony. According to this monitoring model, a remote network monitoring platform is designed and realized. The user can interact with the control center module through an Internet browser, and the information about the monitored manufacturing machines and devices can be displayed by means of text, chart, graphic and sound, etc. Moreover, the details about the problems or faults from the monitored objects can be obtained in time. The experimental results indicate that the network monitoring platform can accurately get the information of the monitored objects, and users can conveniently get the online running state of those monitored objects.
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Hou, Wei Yan, Zhao Hui Qin, and Hai Kuan Wang. "Integration Infrastructure of Token-Based Industrial WSN and TCP." Applied Mechanics and Materials 52-54 (March 2011): 1300–1305. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.1300.
Повний текст джерелаАнотація:
As some prevalent wired networks in industrial field are based on TCP, we proposed a novel integration infrastructure of token-based industrial WSN protocol and TCP, so as to make the application of wireless communication technologies more flexible. In the paper, a protocol conversion infrastructure is presented, which is based on data mapping mechanism. The protocol converter includes two modules: Wireless Industrial Control Network(WICN) master module and TCP host module. WICN master module owns an exclusive data buffer called WICN data buffer. TCP host module also has its own data buffer which is named TCP data buffer. A public data area is used for realizing the data mapping between WICN data buffer and TCP data buffer. By the data mapping mechanism, a station in wired network segment could get the data from WICN by accessing TCP data buffer, and WICN slave station could get the data from wired network via WICN data buffer. By the protocol conversion mechanism based on data mapping, flexible data exchange of heterogeneous networks could be realized.
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Navlakha, Saket, Xin He, Christos Faloutsos, and Ziv Bar-Joseph. "Topological properties of robust biological and computational networks." Journal of The Royal Society Interface 11, no. 96 (July 6, 2014): 20140283. http://dx.doi.org/10.1098/rsif.2014.0283.
Повний текст джерелаАнотація:
Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields.
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Cheng, Feng Min. "Design of Inverter Communication Network Query Engine." Advanced Materials Research 945-949 (June 2014): 2758–61. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2758.
Повний текст джерелаАнотація:
According to the communication mode between different series of inverter and different series of PLC, the database model of communication protocols and parameters is built; then query application software is developed for the establishment, modification and searching the inverter communication network configuration. It is built with VC++ as the foreground, SQL Server as the background database support. The main function modules include communication parameter management module, communication program information management module and information query module. Results show that it can quickly provide communication scheme between the commonly used inverter and PLC for the industrial site and technical personnel.
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Gallagher, Joseph P., Corrinne E. Grover, Guanjing Hu, Josef J. Jareczek, and Jonathan F. Wendel. "Conservation and Divergence in Duplicated Fiber Coexpression Networks Accompanying Domestication of the Polyploid Gossypium hirsutum L." G3: Genes|Genomes|Genetics 10, no. 8 (June 25, 2020): 2879–92. http://dx.doi.org/10.1534/g3.120.401362.
Повний текст джерелаАнотація:
Gossypium hirsutum L. (Upland cotton) has an evolutionary history involving inter-genomic hybridization, polyploidization, and subsequent domestication. We analyzed the developmental dynamics of the cotton fiber transcriptome accompanying domestication using gene coexpression networks for both joint and homoeologous networks. Remarkably, most genes exhibited expression for at least one homoeolog, confirming previous reports of widespread gene usage in cotton fibers. Most coexpression modules comprising the joint network are preserved in each subgenomic network and are enriched for similar biological processes, showing a general preservation of network modular structure for the two co-resident genomes in the polyploid. Interestingly, only one fifth of homoeologs co-occur in the same module when separated, despite similar modular structures between the joint and homoeologous networks. These results suggest that the genome-wide divergence between homoeologous genes is sufficient to separate their co-expression profiles at the intermodular level, despite conservation of intramodular relationships within each subgenome. Most modules exhibit D-homoeolog expression bias, although specific modules do exhibit A-homoeolog bias. Comparisons between wild and domesticated coexpression networks revealed a much tighter and denser network structure in domesticated fiber, as evidenced by its fewer modules, 13-fold increase in the number of development-related module member genes, and the poor preservation of the wild network topology. These results demonstrate the amazing complexity that underlies the domestication of cotton fiber.
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Qi, Jianlong, Tom Michoel, and Gregory Butler. "An Integrative Approach to Infer Regulation Programs in a Transcription Regulatory Module Network." Journal of Biomedicine and Biotechnology 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/245968.
Повний текст джерелаАнотація:
The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression profiles and are regulated by same transcription factors. The process of learning module networks consists of two steps: first clustering genes into modules and then inferring the regulation program (transcription factors) of each module. Many algorithms have been designed to infer the regulation program of a given gene module, and these algorithms show very different biases in detecting regulatory relationships. In this work, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. Experiments in a yeast dataset show that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a lower computational cost than rank aggregation, achieves comparable results as given by rank aggregation.
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He, Binsheng, Junlin Xu, Yingxiang Tian, Bo Liao, Jidong Lang, Huixin Lin, Xiaofei Mo, Qingqing Lu, Geng Tian, and Pingping Bing. "Gene Coexpression Network and Module Analysis across 52 Human Tissues." BioMed Research International 2020 (May 5, 2020): 1–14. http://dx.doi.org/10.1155/2020/6782046.
Повний текст джерелаАнотація:
Gene coexpression analysis is widely used to infer gene modules associated with diseases and other clinical traits. However, a systematic view and comparison of gene coexpression networks and modules across a cohort of tissues are more or less ignored. In this study, we first construct gene coexpression networks and modules of 52 GTEx tissues and cell lines. The network modules are enriched in many tissue-common functions like organelle membrane and tissue-specific functions. We then study the correlation of tissues from the network point of view. As a result, the network modules of most tissues are significantly correlated, indicating a general similar network pattern across tissues. However, the level of similarity among the tissues is different. The tissues closing in a physical location seem to be more similar in their coexpression networks. For example, the two adjacent tissues fallopian tube and bladder have the highest Fisher’s exact test p value 8.54E-291 among all tissue pairs. It is known that immune-associated modules are frequently identified in coexperssion modules. In this study, we found immune modules in many tissues like liver, kidney cortex, lung, uterus, adipose subcutaneous, and adipose visceral omentum. However, not all tissues have immune-associated modules, for example, brain cerebellum. Finally, by the clique analysis, we identify the largest clique of modules, in which the genes in each module are significantly overlapped with those in other modules. As a result, we are able to find a clique of size 40 (out of 52 tissues), indicating a strong correlation of modules across tissues. It is not surprising that the 40 modules are most commonly enriched in immune-related functions.
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Knaack, Sara A., Alireza Fotuhi Siahpirani, and Sushmita Roy. "A Pan-Cancer Modular Regulatory Network Analysis to Identify Common and Cancer-Specific Network Components." Cancer Informatics 13s5 (January 2014): CIN.S14058. http://dx.doi.org/10.4137/cin.s14058.
Повний текст джерелаАнотація:
Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.
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Wang, Hong Wei, Xiao Ming Ni, Lei Ming Cheng, Chun Lei Zhang, Wen Kai Zhang, Xiao Ni Wang, and Qi Mu Surong. "A Type of Wireless Sensor Network Platform." Advanced Materials Research 655-657 (January 2013): 665–68. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.665.
Повний текст джерелаАнотація:
The wireless sensor network (WSN) is the development trend of the technology of sensor. This paper, based on the nRF905 wireless module, introduces a wireless temperature gathering and transmitting system. From RF modules and master control module, the hardware platform has been designed, beyond that, the paper introduces the temperature sensor network software design. The test show that the system is stable, and data are reliable.
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Wang, Chenxu, Chaofan Yang, Xinying Wang, Guanlun Zhou, Chao Chen, and Guorong Han. "ceRNA Network and Functional Enrichment Analysis of Preeclampsia by Weighted Gene Coexpression Network Analysis." Computational and Mathematical Methods in Medicine 2022 (January 7, 2022): 1–14. http://dx.doi.org/10.1155/2022/5052354.
Повний текст джерелаАнотація:
Background. Preeclampsia (PE) is a multisystemic syndrome which has short- and long-term risk to mothers and children and has pluralistic etiology. Objective. This study is aimed at constructing a competitive endogenous RNA (ceRNA) network for pathways most related to PE using a data mining strategy based on weighted gene coexpression network analysis (WGCNA). Methods. We focused on pathways involving hypoxia, angiogenesis, and epithelial mesenchymal transition according to the gene set variation analysis (GSVA) scores. The gene sets of these three pathways were enriched by gene set enrichment analysis (GSEA). WGCNA was used to study the underlying molecular mechanisms of the three pathways in the pathogenesis of PE by analyzing the relationship among pathways and genes. The soft threshold power (β) and topological overlap matrix allowed us to obtain 15 modules, among which the red module was chosen for the downstream analysis. We chose 10 hub genes that satisfied ∣ log 2 Fold Change ∣ > 2 and had a higher degree of connectivity within the module. These candidate genes were subsequently confirmed to have higher gene significance and module membership in the red module. Coexpression networks were established for the hub genes to unfold the connection between the genes in the red module and PE. Finally, ceRNA networks were constructed to further clarify the underlying molecular mechanism involved in the occurrence of PE. 56 circRNAs, 17 lncRNAs, and 20 miRNAs participated in the regulation of the hub genes. Coagulation factor II thrombin receptor (F2R) and lumican (LUM) were considered the most relevant genes, and ceRNA networks of them were constructed. Conclusion. The microarray data mining process based on bioinformatics methods constructed lncRNA and miRNA networks for ten hub genes that were closely related to PE and focused on ceRNAs of F2R and LUM finally. The results of our study may provide insight into the mechanisms underlying PE occurrence.
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Wang, Xian Chao, Yun Fei Yao, Chun Sheng Wang, Wei Wei Sun, and Kang Zhe Wang. "Architecture of the Monitor System in Ternary Optical Computer." Advanced Materials Research 616-618 (December 2012): 2158–61. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.2158.
Повний текст джерелаАнотація:
The monitor system architecture in ternary optical computer (TOC) was discussed. There were some important modules, such as the client, network communication module (NCM), data preprocess module (DPM), operation-request scheduling module (ORSM), optical processor allocation module (OPAM) and the embedded system in the architecture. And the communication protocols between these modules were analyzed and designed. At the same time, the functions of the modules were introduced.
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ANDRECUT, M., and M. K. ALI. "A SIMPLE NEURAL NETWORK APPROACH TO INVARIANT IMAGE RECOGNITION." Modern Physics Letters B 15, no. 01 (January 15, 2001): 11–17. http://dx.doi.org/10.1142/s0217984901001458.
Повний текст джерелаАнотація:
In this paper we propose a simple neural network architecture for invariant image recognition. The proposed neural network architecture contains three specialized modules. The neurons from the first module are connected in a cellular neural network structure, which is responsible for image processing: edge detection and segmentation. The second module is a feed forward neural network for invariant feature extraction from the sensorial layer: computation of the pair distribution function and bond angle distribution function. The third module is responsible for image classification. An application to the face recognition problem is also presented.
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Srivastava, Vivek, Bipin K. Tripathi, and Vinay K. Pathak. "Hybrid Computation Model for Intelligent System Design by Synergism of Modified EFC with Neural Network." International Journal of Information Technology & Decision Making 14, no. 01 (January 2015): 17–41. http://dx.doi.org/10.1142/s0219622014500813.
Повний текст джерелаАнотація:
In recent past, it has been seen in many applications that synergism of computational intelligence techniques outperforms over an individual technique. This paper proposes a new hybrid computation model which is a novel synergism of modified evolutionary fuzzy clustering with associated neural networks. It consists of two modules: fuzzy distribution and neural classifier. In first module, mean patterns are distributed into the number of clusters based on the modified evolutionary fuzzy clustering, which leads the basis for network structure selection and learning in associated neural classifier. In second module, training and subsequent generalization is performed by the associated neural networks. The number of associated networks required in the second module will be same as the number of clusters generated in the first module. Whereas, each network contains as many output neurons as the maximum number of members assigned to each cluster. The proposed hybrid model is evaluated over wide spectrum of benchmark problems and real life biometric recognition problems even in presence of real environmental constraints such as noise and occlusion. The results indicate the efficacy of proposed method over related techniques and endeavor promising outcomes for biometric applications with noise and occlusion.
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PROM-ON, SANTITHAM, ATTHAWUT CHANTHAPHAN, JONATHAN HOYIN CHAN, and ASAWIN MEECHAI. "ENHANCING BIOLOGICAL RELEVANCE OF A WEIGHTED GENE CO-EXPRESSION NETWORK FOR FUNCTIONAL MODULE IDENTIFICATION." Journal of Bioinformatics and Computational Biology 09, no. 01 (February 2011): 111–29. http://dx.doi.org/10.1142/s0219720011005252.
Повний текст джерелаАнотація:
Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.
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Johnson, Erik C. B., E. Kathleen Carter, Eric B. Dammer, Duc M. Duong, Ekaterina S. Gerasimov, Yue Liu, Jiaqi Liu, et al. "Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level." Nature Neuroscience 25, no. 2 (February 2022): 213–25. http://dx.doi.org/10.1038/s41593-021-00999-y.
Повний текст джерелаАнотація:
AbstractThe biological processes that are disrupted in the Alzheimer’s disease (AD) brain remain incompletely understood. In this study, we analyzed the proteomes of more than 1,000 brain tissues to reveal new AD-related protein co-expression modules that were highly preserved across cohorts and brain regions. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism and a module related to the matrisome. The matrisome module was influenced by the APOE ε4 allele but was not related to the rate of cognitive decline after adjustment for neuropathology. By contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD.
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Li, Meng, Xin Jiang, Li Li Ma, Yi Fang Ma, Quan Tong Guo, Yan Jun Lei, and Zhi Ming Zheng. "Control of Synchronization on Community Networks." Applied Mechanics and Materials 548-549 (April 2014): 1454–59. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1454.
Повний текст джерелаАнотація:
Synchronization of coupled oscillators on networks has been investigated in a wide range of topologies. One of the major challenges is how to control the synchronization process through network structures. In this paper, we study the control of network synchronization by considering the mixing regions of different modules in networks. It is shown that small or weak mixing parts on module networks may hinder the synchronization of the whole network while large and strong mixing parts may accelerate synchronization. Our findings indicate that mesoscopic structures should be of importance to controlling network synchronization.
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Gao, Guanlei, Jie Cao, Chun Bao, Qun Hao, Aoqi Ma, and Gang Li. "A Novel Transformer-Based Attention Network for Image Dehazing." Sensors 22, no. 9 (April 30, 2022): 3428. http://dx.doi.org/10.3390/s22093428.
Повний текст джерелаАнотація:
Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and depth in convolutional neural networks for feature extraction. Although the performance is greatly improved compared with the prior-based methods, the performance in extracting detailed information is inferior. In this paper, we proposed an image dehazing model built with a convolutional neural network and Transformer, called Transformer for image dehazing (TID). First, we propose a Transformer-based channel attention module (TCAM), using a spatial attention module as its supplement. These two modules form an attention module that enhances channel and spatial features. Second, we use a multiscale parallel residual network as the backbone, which can extract feature information of different scales to achieve feature fusion. We experimented on the RESIDE dataset, and then conducted extensive comparisons and ablation studies with state-of-the-art methods. Experimental results show that our proposed method effectively improves the quality of the restored image, and it is also better than the existing attention modules in performance.
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Petridis, Vassilios, and Athanasios Kehagias. "A Recurrent Network Implementation of Time Series Classification." Neural Computation 8, no. 2 (February 15, 1996): 357–72. http://dx.doi.org/10.1162/neco.1996.8.2.357.
Повний текст джерелаАнотація:
An incremental credit assignment (ICRA) scheme is introduced and applied to time series classification. It has been inspired from Bayes' rule, but the Bayesian connection is not necessary either for its development or proof of its convergence properties. The ICRA scheme is implemented by a recurrent, hierarchical, modular neural network, which consists of a bank of predictive modules at the lower level, and a decision module at the higher level. For each predictive module, a credit function is computed; the module that best predicts the observed time series behavior receives highest credit. We prove that the credit functions converge (with probability one) to correct values. Simulation results are also presented.
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Song, Zhehan, Zhihai Xu, Jing Wang, Huajun Feng, and Qi Li. "Dual-Branch Feature Fusion Network for Salient Object Detection." Photonics 9, no. 1 (January 14, 2022): 44. http://dx.doi.org/10.3390/photonics9010044.
Повний текст джерелаАнотація:
Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.
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Dai, Xili, Chunmei Ma, Jingwei Sun, Tao Zhang, Haigang Gong, and Ming Liu. "Self-Amplificated Network: Learning fine-grained learner with few samples." Journal of Physics: Conference Series 2050, no. 1 (October 1, 2021): 012006. http://dx.doi.org/10.1088/1742-6596/2050/1/012006.
Повний текст джерелаАнотація:
Abstract Training deep neural networks from only a few examples has been an interesting topic that motivated few shot learning. In this paper, we study the fine-grained image classification problem in a challenging few-shot learning setting, and propose the Self-Amplificated Network (SAN), a method based on meta-learning to tackle this problem. The SAN model consists of three parts, which are the Encoder, Amplification and Similarity Modules. The Encoder Module encodes a fine-grained image input into a feature vector. The Amplification Module is used to amplify subtle differences between fine-grained images based on the self attention mechanism which is composed of multi-head attention. The Similarity Module measures how similar the query image and the support set are in order to determine the classification result. In-depth experiments on three benchmark datasets have showcased that our network achieves superior performance over the competing baselines.
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Li, Jin, Feng Chen, Qiushi Zhang, Xianglian Meng, Xiaohui Yao, Shannon L. Risacher, Jingwen Yan, Andrew J. Saykin, Hong Liang, and Li Shen. "Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer’s Disease." Current Alzheimer Research 16, no. 13 (January 10, 2020): 1163–74. http://dx.doi.org/10.2174/1567205016666191121142558.
Повний текст джерелаАнотація:
Background: The etiology of Alzheimer’s disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. Objective: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer’s disease biomarker, by employing a networ kassisted strategy. Method: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. Results: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer’s disease but have shown associations with other neurodegenerative diseases. Conclusion: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer’s disease and suggest potential therapeutic targets.
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Alam, M. K., Azrina Abd Aziz, S. A. Latif, and Azlan Awang. "Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks." Sensors 20, no. 4 (February 13, 2020): 1011. http://dx.doi.org/10.3390/s20041011.
Повний текст джерелаАнотація:
A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.
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Muthiah, Annamalai, Susanna R. Keller, and Jae K. Lee. "Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism." International Journal of Genomics 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8514071.
Повний текст джерелаАнотація:
Different computational approaches have been examined and compared for inferring network relationships from time-series genomic data on human disease mechanisms under the recent Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge. Many of these approaches infer all possible relationships among all candidate genes, often resulting in extremely crowded candidate network relationships with many more False Positives than True Positives. To overcome this limitation, we introduce a novel approach, Module Anchored Network Inference (MANI), that constructs networks by analyzing sequentially small adjacent building blocks (modules). Using MANI, we inferred a 7-gene adipogenesis network based on time-series gene expression data during adipocyte differentiation. MANI was also applied to infer two 10-gene networks based on time-course perturbation datasets from DREAM3 and DREAM4 challenges. MANI well inferred and distinguished serial, parallel, and time-dependent gene interactions and network cascades in these applications showing a superior performance to other in silico network inference techniques for discovering and reconstructing gene network relationships.
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Yu, Qi, Gong-Hua Li, and Jing-Fei Huang. "MOfinder: A Novel Algorithm for Detecting Overlapping Modules from Protein-Protein Interaction Network." Journal of Biomedicine and Biotechnology 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/103702.
Повний текст джерелаАнотація:
Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease.