A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding

Nasiri, Elahe; Berahmand, Kamal; Rostami, Mehrdad; Dabiri, Mohammad

doi:10.1016/j.compbiomed.2021.104772

Cited by 105 publications

(41 citation statements)

References 59 publications

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“…Impressive improvements in machine learning models are rejuvenating the application of Artificial Intelligence (AI) in healthcare that basically started over half a century ago [ 10 ]. In the field of COVID-19 disease diagnosis, the utilization of chest X-ray and CT-scan imaging is already populated in many developing countries such as India, Africa, South- America due to insufficient number of RT-PCR test-kits, and the established link between the ground-glass opacity occurrence in the periphery of lungs and SARS-Cov-2 [ [11] , [12] , [13] , [14] , [15] ], although with some limited success [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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Section: Introductionmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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“…F-measure criterion makes it easier to visualize and compare the performance of link prediction methods in different operating conditions than ROC. 30 Here, the results are reviewed for different number of suggested users from 1 to 30. The results of this comparison show that when TopK = 5, the proposed method achieves the best f-measure result of 0.645.…”

Section: Results and Comparisonsmentioning

confidence: 99%

“…Figure 10 shows the f‐measure criterion results for the proposed similarity measure (PM) and other classical similarity measures including CN, JA, AA, KT, and FL. F‐measure criterion makes it easier to visualize and compare the performance of link prediction methods in different operating conditions than ROC 30 . Here, the results are reviewed for different number of suggested users from 1 to 30.…”

Section: Simulation Analysismentioning

confidence: 99%

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Yang

Peng

et al. 2022

Concurrency and Computation

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Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user' on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

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“… 31 The feature dimensions are reduced to integrate the network structure and features for link prediction, 31 an HEA network by using their descriptors that is used to detect community for similar nodes. 31 …”

Section: Review Of Literaturementioning

confidence: 99%

Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

et al. 2022

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There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein–protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.

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A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding

Cited by 105 publications

References 59 publications

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

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