BRWSP: Predicting circRNA-Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

Lei, Xiujuan; Zhang, Wenxiang

doi:10.1155/2019/5938035

Cited by 14 publications

(6 citation statements)

References 53 publications

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“…As an improvement approach to the previous study, the current work attempts to reduce the communication that is being generated from the sensor level by employing caching techniques, thus maintaining the information locally and performing a few simple constant time procedures on the sensor data. Several caching techniques were experimented in WSNs [16,22]; however, the current work extends the studies by combining caching with content awareness in an SDN controlled environment. Caching techniques are widely used in the communications domain in order to improve access time to information and to increase network performance.…”

Section: Related Workmentioning

confidence: 94%

“…With the evolution of wireless sensor networks over time, the mechanisms which ensure effective data transmission and longer sensor lifespan have been required to evolve as well to match the end-user applications' expectations [16,17]. Improvements are currently being developed in enhancing the programmability of such networks.…”

Section: Related Workmentioning

confidence: 99%

“…The current work extends this approach by proactively delivering the data from the central component towards the application plane and improving the QoS and the QoE of the proposed system. [16] is a study which explores content caching to reduce the energy consumption at certain designated nodes; however, the current work differs in that the content caching takes place at every sensor node. Another enhancement provided by the current research is that it proposes the use of a single type of sensor in the sensors' plane, therefore simplifying the system design and its deployment.…”

Section: Related Workmentioning

confidence: 99%

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Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks

Buzura

Iancu

Dadarlat

et al. 2020

Sensors

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Software-defined wireless sensor networking (SDWSN) is an emerging networking architecture which is envisioned to become the main enabler for the internet of things (IoT). In this architecture, the sensors plane is managed by a control plane. With this separation, the network management is facilitated, and performance is improved in dynamic environments. One of the main issues a sensor environment is facing is the limited lifetime of network devices influenced by high levels of energy consumption. The current work proposes a system design which aims to improve the energy efficiency in an SDWSN by combining the concepts of content awareness and adaptive data broadcast. The purpose is to increase the sensors’ lifespan by reducing the number of generated data packets in the resource-constrained sensors plane of the network. The system has a distributed management approach, with content awareness being implemented at the individual programmable sensor level and the adaptive data broadcast being performed in the control plane. Several simulations were run on historical weather and the results show a significant decrease in network traffic. Compared to similar work in this area which focuses on improving energy efficiency with complex algorithms for routing, clustering, or caching, the current proposal employs simple computing procedures on each network device with a high impact on the overall network performance.

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Section: Related Workmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks

Buzura

Iancu

Dadarlat

et al. 2020

Sensors

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“…Lei et al. [ 79 ] proposed a computational model (see Figure 2 ) of Biased Random Walk to Search Paths on a multiple heterogeneous network (BRWSP) to predict circRNA-disease associations. Firstly, they construct the multi-layer heterogeneous network by utilizing the information of circRNA similarity matrix CS , disease similarity matrix DS , gene similarity matrix GS , circRNA-disease association matrix CD , circRNA-gene interaction matrix CG as well as gene-disease association matrix GD .…”

Section: Computational Modelsmentioning

confidence: 99%

Circular RNAs and complex diseases: from experimental results to computational models

Wang

Han

Zhao

et al. 2021

Briefings in Bioinformatics

122

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Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.

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“…The first category is a network propagating method. For example, the computational model BRWSP applies biased random walk to search paths on a multiple heterogeneous network to discover circRNA-disease associations [20]. A method called KATZHCDA uses KATZ measures for human circRNA-disease association prediction [21].…”

Section: Introductionmentioning

confidence: 99%

GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network

Bian

Lei

2021

Cancers

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CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA–disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA–miRNA–mRNA axis plays an important role in the generation and development of diseases, circRNA–miRNA interactions and disease–mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA–disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases.

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BRWSP: Predicting circRNA-Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

Cited by 14 publications

References 53 publications

Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks

Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks

Circular RNAs and complex diseases: from experimental results to computational models

GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network

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