A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining

2021 ◽  
Author(s):  
Tian Bai ◽  
Ying Li ◽  
Ye Wang ◽  
Lan Huang
Keyword(s):  
Network Embedding ◽  
Embedding Method ◽  
Relation Mining

scholarly journals Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

2021 ◽  
Vol 3 ◽  
Author(s):  
Tristan Millington ◽  
Saturnino Luz
Keyword(s):  
Word Frequency ◽  
Mean Squared Error ◽  
Cognitive Test ◽  
Edge Density ◽  
Network Embedding ◽  
Embedding Method ◽  
Control Networks ◽  
Network Measures ◽  
Network Properties ◽  
And Control

In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these networks for significant differences, finding that networks from Alzheimer’s patients have a lower heterogeneity and centralization, but a higher edge density. We then use these measures, a network embedding method and some measures from the word frequency distribution to classify the transcripts into control or Alzheimer’s, and to estimate the cognitive test score of a participant based on the transcript. We find it is possible to distinguish between the AD and control networks on structure alone, achieving 66.7% accuracy on the test set, and to predict cognitive scores with a root mean squared error of 5.675. Using the network measures is more successful than using the network embedding method. However, if the networks are shuffled we find relatively few of the measures are different, indicating that word frequency drives many of the network properties. This observation is borne out by the classification experiments, where word frequency measures perform similarly to the network measures.

Network Embedding Method Based On Extractive Summary

2019 ◽  
Author(s):  
Yuanfa Ji ◽  
Yuzhu Liu ◽  
Xiaodong Cai ◽  
Dai Huang ◽  
YueLin Hu
Keyword(s):  
Network Embedding ◽  
Embedding Method

Prediction of Drug Combinations with a Network Embedding Method

2019 ◽  
Vol 21 (10) ◽  
pp. 789-797 ◽  
Author(s):  
Tianyun Wang ◽  
Lei Chen ◽  
Xian Zhao
Keyword(s):  
Computational Methods ◽  
Drug Combination ◽  
Chemical Interaction ◽  
Drug Combinations ◽  
Computational Techniques ◽  
Svm Classifier ◽  
Network Embedding ◽  
Embedding Method ◽  
Topological Features ◽  
Individual Features

Aim and Objective: There are several diseases having a complicated mechanism. For such complicated diseases, a single drug cannot treat them very well because these diseases always involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug combination is an effective way to treat such diseases. However, determination of effective drug combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and cheap methods in this regard. Designing effective computational methods incorporating advanced computational techniques to predict drug combinations is an alternative and feasible way. Method: In this study, we proposed a novel network embedding method, which can extract topological features of each drug combination from a drug network that was constructed using chemical-chemical interaction information retrieved from STITCH. These topological features were combined with individual features of drug combination reported in one previous study. Several advanced computational methods were employed to construct an effective prediction model, such as synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset, minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS) methods that were adopted to analyze features and extract optimal features for building an optimal support machine vector (SVM) classifier. Results and Conclusion: The constructed optimal SVM classifier yielded an MCC of 0.806, which is superior to the classifier only using individual features with or without SMOTE. The performance of the classifier can be improved by combining the topological features and essential features of a drug combination.

A hierarchical network embedding method based on network partitioning

2021 ◽  
Vol 65 (1) ◽  
pp. 87
Author(s):  
Yunbin Ma ◽  
Xuexiong Luo ◽  
Junlong Kang ◽  
Xiankun Zhang
Keyword(s):  
Network Partitioning ◽  
Network Embedding ◽  
Hierarchical Network ◽  
Embedding Method

A Network Embedding Method Based on Graph Folding

2019 ◽  
Vol 09 (09) ◽  
pp. 1753-1760
Author(s):  
晓硕 冯
Keyword(s):  
Network Embedding ◽  
Embedding Method

scholarly journals ATTENet: Detecting and Explaining Suspicious Tax Evasion Groups

2019 ◽  
Author(s):  
Qinghua Zheng ◽  
Yating Lin ◽  
Huan He ◽  
Jianfei Ruan ◽  
Bo Dong
Keyword(s):  
Random Forest ◽  
Social Relationships ◽  
Tax Evasion ◽  
Network Embedding ◽  
Random Forest Algorithm ◽  
Embedding Method ◽  
Interactive Tools ◽  
Basic Features ◽  
Economic Behaviors ◽  
Affiliated Transaction

In this demonstration, we present ATTENet, a novel visual analytic system for detecting and explaining suspicious affiliated-transaction-based tax evasion (ATTE) groups. First, the system constructs a taxpayer interest interacted network, which contains economic behaviors and social relationships between taxpayers. Then, the system combines basic features and structure features of each group in the network with network embedding method structure2Vec, and then detects suspicious ATTE groups with random forest algorithm. Last, to explore and explain the detection results, the system provides an ATTENet visualization with three coordinated views and interactive tools. We demonstrate ATTENet on a non-confidential dataset which contains two years of real tax data obtained by our cooperative tax authorities to verify the usefulness of our system.

scholarly journals A network embedding-based multiple information integration method for the MiRNA-disease association prediction

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yuchong Gong ◽  
Yanqing Niu ◽  
Wen Zhang ◽  
Xiaohong Li
Keyword(s):  
Information Integration ◽  
Integration Method ◽  
State Of The Art ◽  
Mirna Family ◽  
Disease Association ◽  
Bipartite Network ◽  
Network Embedding ◽  
Embedding Method ◽  
Disease Associations ◽  
Family Associations

Abstract Background MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited. Results In this paper, a network embedding-based multiple information integration method (NEMII) is proposed for the miRNA-disease association prediction. First, known miRNA-disease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNA-family associations and disease semantic similarities) to represent miRNA-disease pairs. Third, the prediction models are constructed based on the miRNA-disease pairs by using the random forest. In computational experiments, NEMII achieves high-accuracy performances and outperforms other state-of-the-art methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, High-Order Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE). Conclusion We propose a new method, named NEMII, for predicting miRNA-disease associations, which has great potential to benefit the field of miRNA-disease association prediction.

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