Nonuniform Hyper-Network Embedding with Dual Mechanism

2020 ◽  
Vol 38 (3) ◽  
pp. 1-18
Author(s):  
Jie Huang ◽  
Chuan Chen ◽  
Fanghua Ye ◽  
Weibo Hu ◽  
Zibin Zheng
Keyword(s):  
Network Embedding ◽  
Dual Mechanism

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

Therapeutic Targets for the Treatment of Comorbidities Associated with Epilepsy

2020 ◽  
Vol 13 (2) ◽  
pp. 85-93
Author(s):  
Kinjal Gangar ◽  
Lokesh Kumar Bhatt
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Neurological Disorders ◽  
Therapeutic Targets ◽  
Therapeutic Failure ◽  
Hyperactivity Disorder ◽  
Novel Targets ◽  
New Therapeutic Targets ◽  
Dual Mechanism ◽  
Seizure Suppression

One of the most common neurological disorders, which occurs among 1% of the population worldwide, is epilepsy. Therapeutic failure is common with epilepsy and nearly about 30% of patients fall in this category. Seizure suppression should not be the only goal while treating epilepsy but associated comorbidities, which can further worsen the condition, should also be considered. Treatment of such comorbidities such as depression, anxiety, cognition, attention deficit hyperactivity disorder and, various other disorders which co-exist with epilepsy or are caused due to epilepsy should also be treated. Novel targets or the existing targets are needed to be explored for the dual mechanism which can suppress both the disease and the comorbidity. New therapeutic targets such as IDO, nNOS, PAR1, NF-κb are being explored for their role in epilepsy and various comorbidities. This review explores recent therapeutic targets for the treatment of comorbidities associated with epilepsy.

Adversarial Training Regularization for Negative Sampling Based Network Embedding

2021 ◽  
Author(s):  
Quanyu Dai ◽  
Xiao Shen ◽  
Zimu Zheng ◽  
Liang Zhang ◽  
Qiang Li ◽  
...  
Keyword(s):  
Network Embedding ◽  
Adversarial Training

Attributed Collaboration Network Embedding for Academic Relationship Mining

2021 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Wei Wang ◽  
Jiaying Liu ◽  
Tao Tang ◽  
Suppawong Tuarob ◽  
Feng Xia ◽  
...  
Keyword(s):  
Collaboration Network ◽  
Network Embedding

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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