Graph kernels combined with the neural network on protein classification

Qiang-rong, Jiang; Qiu, Guang

doi:10.1142/s0219720019500306

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The primary value of the dataset come from calculated relationships which create subgraphs that serve as a substrate for the application of exploration and application of graph algorithms [20][21][22]. These occur primarily at two different scales: (1) patient-patient network with direct relationships among patients (or tumor samples) based on similarity scores or correlation for genomic features or signatures; (2) biological networks within single patient samples.…”

Section: Data Descriptionmentioning

confidence: 99%

A cancer graph: a lung cancer property graph database in Neo4j

Tuck

2022

BMC Res Notes

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Objectives A novel graph data model of non-small cell lung cancer clinical and genomic data has been constructed with two aims: (1) provide a suitable model for facilitating graph analytics within the Neo4j framework or through tools which can interact through existing Neo4j APIs; and (2) provide a base model extensible to other cancer types and additional datasets such as those derived from electronic health records and other real world sources. Data description Clinical and genomic data integrated with a novel property graph database schema from publicly available datasets and analyses based on The Cancer Genome Atlas lung cancer datasets augmented by with subgraphs patient-patient social network from similarity and correlation as well as individual based biological networks.

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Section: Data Descriptionmentioning

confidence: 99%

A cancer graph: a lung cancer property graph database in Neo4j

Tuck

2022

BMC Res Notes

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“…The advantage of the model-based quantitative analysis method of network vulnerability is that it can analyze and calculate the vulnerability independently of network attacks, and can better reflect the degree of network vulnerability [17]. This enables the neural network to be well applied to the parallel computer for calculation, which can greatly improve the speed of calculation [18].…”

Section: ) Vulnerabilitymentioning

confidence: 99%

Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

Wang¹

2020

Preprint

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With the wide application of computer network, network security has attracted more and more attention. The main reason why all kinds of attacks on the network can pose a great threat to the network security is the vulnerability of the computer network system itself. Introducing neural network technology into computer network vulnerability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assessment as the computer simulation and analysis on the actual example, design a set of computer network vulnerability assessment scheme, finally the vulnerability is lower than 0.75, which is beneficial to research on related theory and application to provide the reference and help.

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“…The function of each processing unit is simple. This enables the neural network to be well applied to the parallel computer for calculation, which can greatly improve the speed of calculation [18]. 2 Neural network has very strong fault tolerance.…”

Section: Characteristics Of Neural Networkmentioning

confidence: 99%

The key technology of computer network vulnerability assessment based on neural network

Wang

2020

J Wireless Com Network

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With the wide application of computer network, network security has attracted more and more attention. The main reason why all kinds of attacks on the network can pose a great threat to the network security is the vulnerability of the computer network system itself. Introducing neural network technology into computer network vulnerability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network, and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network, algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assessment as the computer simulation and analysis on the actual example designs a set of computer network vulnerability assessment scheme, finally the vulnerability is lower than 0.75, which is beneficial to research on related theory and application to provide the reference and help.

show abstract

Graph kernels combined with the neural network on protein classification

Cited by 5 publications

References 24 publications

A cancer graph: a lung cancer property graph database in Neo4j

A cancer graph: a lung cancer property graph database in Neo4j

Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

The key technology of computer network vulnerability assessment based on neural network

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