scholarly journals Network Representation

2020 ◽  
pp. 217-283
Author(s):  
Zhiyuan Liu ◽  
Yankai Lin ◽  
Maosong Sun
Keyword(s):  
Network Representation

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

scholarly journals The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222956-222965
Author(s):  
Dong Liu ◽  
Qinpeng Li ◽  
Yan Ru ◽  
Jun Zhang
Keyword(s):  
Random Walk ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Network Representation

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning

2021 ◽  
Author(s):  
Wen Zhang ◽  
B. Blair Braden ◽  
Gustavo Miranda ◽  
Kai Shu ◽  
Suhang Wang ◽  
...  
Keyword(s):  
Representation Learning ◽  
Network Representation ◽  
Neuroimaging Data

scholarly journals Dyonic objects and tensor network representation

2020 ◽  
pp. 2050336
Author(s):  
A. Belhaj ◽  
Y. El Maadi ◽  
S-E. Ennadifi ◽  
Y. Hassouni ◽  
M. B. Sedra
Keyword(s):  
Particle Physics ◽  
Network Representation ◽  
Tensor Network ◽  
Arbitrary Dimensions

Motivated by particle physics results, we investigate certain dyonic solutions in arbitrary dimensions. Concretely, we study the stringy constructions of such objects from concrete compactifications. Then, we elaborate their tensor network realizations using multistate particle formalism.

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