scholarly journals Analysing the structure of pathways and its influence on the interpretation of biomedical datasets

2018 ◽  
Author(s):  
Bram Burger ◽  
Luis Francisco Hernández Sánchez ◽  
Ragnhild Reehorst Lereim ◽  
Harald Barsnes ◽  
Marc Vaudel
Keyword(s):  
Functional Analysis ◽  
Pathway Analysis ◽  
Molecular Interactions ◽  
Network Structure ◽  
Experimental Results ◽  
Biochemical Pathways ◽  
Underlying Network ◽  
Biological Interpretation ◽  
Pathway Analyses ◽  
Current Pathway

SummaryBiochemical pathways are commonly used as a reference to conduct functional analysis on biomedical omics datasets, where experimental results are mapped to knowledgebases comprising known molecular interactions collected from the literature. Due to their central role, the content of the functional knowledgebases directly influences the outcome of pathway analyses. In this study, we investigate the structure of the current pathway knowledge, as exemplified by Reactome, discuss the consequences for biological interpretation, and outline possible improvements in the use of pathway knowledgebases. By providing a view of the underlying network structure, we aim to help pathway analysis users manage their expectations and better identify possible artefacts in the results.

scholarly journals Pathway Analysis of a Transcriptome and Metabolite Profile to Elucidate a Compensatory Mechanism for Taurine Deficiency in the Heart of Taurine Transporter Knockout Mice

J ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 57-70
Author(s):  
Takashi Ito ◽  
Shigeru Murakami ◽  
Stephen Schaffer
Keyword(s):  
Pathway Analysis ◽  
Knockout Mice ◽  
Metabolite Profile ◽  
Integrated Analysis ◽  
Compensatory Mechanism ◽  
Compensatory Mechanisms ◽  
Taurine Transporter ◽  
Kegg Pathway Database ◽  
Taurine Deficiency ◽  
Pathway Analyses

Taurine, which is abundant in mammalian tissues, especially in the heart, is essential for cellular osmoregulation. We previously reported that taurine deficiency leads to changes in the levels of several metabolites, suggesting that alterations in those metabolites might compensate in part for tissue taurine loss, a process that would be important in maintaining cardiac homeostasis. In this study, we investigated the molecular basis for changes in the metabolite profile of a taurine-deficient heart using pathway analysis based on the transcriptome and metabolome profile in the hearts of taurine transporter knockout mice (TauTKO mice), which have been reported by us. First, the genes associated with transport activity, such as the solute carrier (SLC) family, are increased in TauTKO mice, while the established transporters for metabolites that are elevated in the TauTKO heart, such as betaine and carnitine, are not altered by taurine deficiency. Second, the integrated analysis using transcriptome and metabolome data revealed significant increases and/or decreases in the genes involved in Arginine metabolism, Ketone body degradation, Glycerophospholipid metabolism, and Fatty acid metabolism in the KEGG pathway database. In conclusion, these pathway analyses revealed genetic compensatory mechanisms involved in the control of the metabolome profile of the taurine-deficient heart.

scholarly journals SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 93 ◽  
Author(s):  
Zhenrong Deng ◽  
Rui Yang ◽  
Rushi Lan ◽  
Zhenbing Liu ◽  
Xiaonan Luo
Keyword(s):  
Receptive Field ◽  
Face Detection ◽  
Network Structure ◽  
Difficult Problem ◽  
Detection Performance ◽  
Experimental Results ◽  
Small Scale ◽  
Detection Accuracy ◽  
Novel Method ◽  
Face Detector

Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.

scholarly journals Generative Adversarial Network for Image Super-Resolution Combining Texture Loss

2020 ◽  
Vol 10 (5) ◽  
pp. 1729 ◽  
Author(s):  
Yuning Jiang ◽  
Jinhua Li
Keyword(s):  
Loss Function ◽  
Network Structure ◽  
Similarity Index ◽  
Super Resolution ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Visual Evaluation ◽  
Low Frequencies ◽  
Adversarial Networks

Objective: Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, we propose a novel and improved algorithm. Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. We redefined the generator network and discriminator network. Firstly, on the network structure, residual dense blocks without excess batch normalization layers were used to form generator network. Visual Geometry Group (VGG)19 network was adopted as the basic framework of discriminator network. Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. Texture loss was proposed to encourage local information matching. Perceptual loss was enhanced by employing the features before activation layer to calculate. Adversarial loss was optimized based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory. Content loss was used to ensure the accuracy of low-frequency information. During the optimization process, the target image information was reconstructed from different angles of high and low frequencies. Results: The experimental results showed that our method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations such as brightness information and texture details. We found that it could generate images with more realistic textures and more accurate brightness, which were more in line with human visual evaluation. Conclusions: Our improvements to the network structure could reduce the model’s calculation amount and stabilize the training direction. In addition, the loss function we present for generator could provide stronger supervision for restoring realistic textures and achieving brightness consistency. Experimental results prove the effectiveness and superiority of TSRGAN algorithm.

scholarly journals Learning Diverse Bayesian Networks

2019 ◽  
Vol 33 ◽  
pp. 7793-7800
Author(s):  
Cong Chen ◽  
Changhe Yuan
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Structure ◽  
Search Algorithm ◽  
Noisy Data ◽  
Local Neighborhood ◽  
Causal Ordering ◽  
True Model ◽  
Underlying Network ◽  
Novel Method

Much effort has been directed at developing algorithms for learning optimal Bayesian network structures from data. When given limited or noisy data, however, the optimal Bayesian network often fails to capture the true underlying network structure. One can potentially address the problem by finding multiple most likely Bayesian networks (K-Best) in the hope that one of them recovers the true model. However, it is often the case that some of the best models come from the same peak(s) and are very similar to each other; so they tend to fail together. Moreover, many of these models are not even optimal respective to any causal ordering, thus unlikely to be useful. This paper proposes a novel method for finding a set of diverse top Bayesian networks, called modes, such that each network is guaranteed to be optimal in a local neighborhood. Such mode networks are expected to provide a much better coverage of the true model. Based on a globallocal theorem showing that a mode Bayesian network must be optimal in all local scopes, we introduce an A* search algorithm to efficiently find top M Bayesian networks which are highly probable and naturally diverse. Empirical evaluations show that our top mode models have much better diversity as well as accuracy in discovering true underlying models than those found by K-Best.

scholarly journals Modeling Network Evolution by Colored Petri Nets

2016 ◽  
Vol 15 (1) ◽  
pp. 41-61
Author(s):  
Suwimon VONGSINGTHONG ◽  
Sirapat BOONKRONG ◽  
Herwig UNGER
Keyword(s):  
Petri Nets ◽  
Network Structure ◽  
Information Diffusion ◽  
User Behavior ◽  
Network Evolution ◽  
Clustering Coefficient ◽  
Colored Petri Nets ◽  
Traffic Patterns ◽  
Underlying Network ◽  
Flow Of Information

Discovering how information was distributed was essential for tracking, optimizing, and controlling networks. In this paper, a premier approach to analyze the reciprocity of user behavior, content, network structure, and interaction rules to the interplay between information diffusion and network evolution was proposed. Parameterization and insight diffusion patterns were characterized based on the community structure of the underlying network using diffusion related behavior data, collected by a developed questionnaire. The user roles in creating the flow of information were stochastically modeled and simulated by Colored Petri Nets, where the growth and evolution of the network structure was substantiated through the formation of the clustering coefficient, the average path length, and the degree distribution. This analytical model could be used for various tasks, including predicting future user activities, monitoring traffic patterns of networks, and forecasting the distribution of content.

scholarly journals Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Liqun Gao ◽  
Yujia Liu ◽  
Hongwu Zhuang ◽  
Haiyang Wang ◽  
Bin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Public Opinion ◽  
Prediction Model ◽  
Network Structure ◽  
Early Warning ◽  
Feature Fusion ◽  
Learning Model ◽  
Feature Model ◽  
Underlying Network ◽  
Deep Learning Model

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

Facial Expression Recognition Method Based on Improved VGG Convolutional Neural Network

2019 ◽  
Vol 34 (07) ◽  
pp. 2056003
Author(s):  
Shuo Cheng ◽  
Guohui Zhou
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Facial Expression Recognition ◽  
Network Models ◽  
Experimental Results ◽  
Expression Recognition ◽  
Classification Problems ◽  
Nonlinear Network

Because the shallow neural network has limited ability to represent complex functions with limited samples and calculation units, its generalization ability will be limited when it comes to complex classification problems. The essence of deep learning is to learn a nonlinear network structure, to represent input data distributed representation and demonstrate a powerful ability to learn deeper features of data from a small set of samples. In order to realize the accurate classification of expression images under normal conditions, this paper proposes an expression recognition model of improved Visual Geometry Group (VGG) deep convolutional neural network (CNN). Based on the VGG-19, the model optimizes network structure and network parameters. Most expression databases are unable to train the entire network from the start due to lack of sufficient data. This paper uses migration learning techniques to overcome the shortage of image training samples. Shallow CNN, Alex-Net and improved VGG-19 deep CNN are used to train and analyze the facial expression data on the Extended Cohn–Kanade expression database, and compare the experimental results obtained. The experimental results indicate that the improved VGG-19 network model can achieve 96% accuracy in facial expression recognition, which is obviously superior to the results of other network models.

P566Activated amyloid-beta pathways in patients with atrial fibrillation and heart failure, a pathway analysis in BIOSTAT

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
V A Artola Arita ◽  
B T Santeman ◽  
I E Sama ◽  
M Kloosterman ◽  
I Van Gelder ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Ejection Fraction ◽  
Sinus Rhythm ◽  
Pathway Analysis ◽  
Amyloid Beta ◽  
Plasma Proteins ◽  
Horizon 2020 ◽  
History Of ◽  
Pathway Analyses

Abstract Funding Acknowledgements European Commission [FP7-242209-BIOSTAT-CHF], European Union’s Horizon 2020 under the Marie Skłodowska-Curie grant agreement No 754425 Background. Atrial fibrillation (AF) and heart failure (HF) are two growing epidemics that frequently co-exist, share clinical risk factors, and predispose to each other. There is limited understanding of the underlying pathophysiology of the combination of both conditions. Purpose. To perform pathway analyses of circulating plasma proteins and evaluate whether patients with both HF and AF have different activated pathways compared to those with HF without AF. Methods. We performed pathway overrepresentation analyses of differentially expressed plasma proteins in HF, with reduced (HFrEF) and preserved (HFpEF) ejection fraction, with AF versus sinus rhythm on ECG at enrolment in BIOSTAT-CHF, using 92 cardiovascular biomarkers. Pathway analyses were performed based on existing knowledge using Gene Ontology, REACTOME, and KEGG, to study underlying activated biological pathways. Resulting pathways were corrected by Bonferroni method. Results. We studied 2,839 patients with HF irrespective of their ejection fraction of whom 1,116 (39%) had AF and 1,723 (61%) were in sinus rhythm. HF patients with AF were older (76 ± 10 vs. 70 ± 12, p < 0.001), were less women (28% vs. 34%, p < 0.001), had history of stroke (16% vs. 10%, p < 0.001), renal disease (39% vs. 31%, p < 0.001) and less history of coronary artery disease (40% vs. 53%, p < 0.001). There were no significant differences in patients with hypertension (62% vs. 60 %, p = 0.22), diabetes (32% vs. 31%, p = 0.51) and COPD (18% vs. 16%, p = 0.20). A total of 1,661 (59%) had HFrEF and 432 (15%) had HFpEF. Pathway overrepresentation analyses revealed three amyloid-related pathways statically significant in  total HF group, and in HFrEF and HFpEF respectively, with AF compared with those in sinus rhythm: amyloid-beta formation (p < 4.0E-4, p < 7.4E-6), amyloid-beta metabolic process (p < 1.0E-3, p < 1.9E-5), and amyloid precursor protein catabolic process (p < 9.1E-4, p < 1.6E-5). The key proteins related to these processes were spondin-1 (SPON-1), insulin-like growth factor binding protein 1 (IGFBP-1) and 7 (IGFBP-7). After adjusting for sex and age and correcting for multiple testing with fall discovery rate (FDR), SPON-1 (FDR < 6.3E-6), IGFBP-1 (FDR < 6.6E-3) and IGFBP-7 (FDR < 2.5E-9) remained statically significant in HFrEF patients with AF vs. sinus rhythm; whereas only SPON-1 (FDR < 7.3 E-3) and IGFBP-7 (FDR < 1.9E-3) remained in HFpEF patients with AF vs. sinus rhythm. Conclusion. Pathway analyses revealed activation of amyloid-beta pathways in HF patients with AF versus sinus rhythm with SPON-1, IGFBP-1 and IGFBP-7 overrepresented proteins. Amyloid-beta pathways may play a role in the pathophysiology of the combination of HF and AF, which needs to be replicated and validated in additional cohorts.  Figure. Pathway analysis of activated proteins in patients with HF, HFrEF (A) and HFpEF (B) and AF versus sinus rhythm. Proteins are represented as dots and pathways as circumferences. Abstract Figure. Pathway overrepresentation analysis

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