MultNet: An Efficient Network Representation Learning for Large-Scale Social Relation Extraction

2018 ◽  
pp. 515-524 ◽  
Author(s):  
Jun Yuan ◽  
Neng Gao ◽  
Lei Wang ◽  
Zeyi Liu
Keyword(s):  
Large Scale ◽  
Social Relation ◽  
Relation Extraction ◽  
Representation Learning ◽  
Network Representation

scholarly journals TransNet: Translation-Based Network Representation Learning for Social Relation Extraction

2017 ◽  
Author(s):  
Cunchao Tu ◽  
Zhengyan Zhang ◽  
Zhiyuan Liu ◽  
Maosong Sun
Keyword(s):  
Semantic Information ◽  
Social Relation ◽  
Source Code ◽  
Relation Extraction ◽  
Representation Learning ◽  
Experimental Results ◽  
Network Representation ◽  
Low Dimensional

Conventional network representation learning (NRL) models learn low-dimensional vertex representations by simply regarding each edge as a binary or continuous value. However, there exists rich semantic information on edges and the interactions between vertices usually preserve distinct meanings, which are largely neglected by most existing NRL models. In this work, we present a novel Translation-based NRL model, TransNet, by regarding the interactions between vertices as a translation operation. Moreover, we formalize the task of Social Relation Extraction (SRE) to evaluate the capability of NRL methods on modeling the relations between vertices. Experimental results on SRE demonstrate that TransNet significantly outperforms other baseline methods by 10% to 20% on hits@1. The source code and datasets can be obtained from https://github.com/thunlp/TransNet.

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.

Adversarial Caching Training: Unsupervised Inductive Network Representation Learning on Large-Scale Graphs

2021 ◽  
pp. 1-12
Author(s):  
Junyang Chen ◽  
Zhiguo Gong ◽  
Wei Wang ◽  
Cong Wang ◽  
Zhenghua Xu ◽  
...  
Keyword(s):  
Large Scale ◽  
Representation Learning ◽  
Network Representation

scholarly journals TransPath: Representation Learning for Heterogeneous Information Networks via Translation Mechanism

2018 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Large Scale ◽  
Representation Learning ◽  
Information Networks ◽  
Heterogeneous Information ◽  
Structure Information ◽  
Heterogeneous Information Networks ◽  
Network Representation ◽  
Meta Path ◽  
Translation Mechanism ◽  
Real World Datasets

In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users' preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world datasets DBLP and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.

Large-scale entity representation learning for biomedical relationship extraction

2020 ◽  
Author(s):  
Mario Sänger ◽  
Ulf Leser
Keyword(s):  
Large Scale ◽  
Research Question ◽  
Relation Extraction ◽  
Representation Learning ◽  
Supplementary Information ◽  
Source Codes ◽  
Prior Art ◽  
Relationship Extraction ◽  
Learning Techniques ◽  
Recent Representation

Abstract Motivation The automatic extraction of published relationships between molecular entities has important applications in many biomedical fields, ranging from Systems Biology to Personalized Medicine. Existing works focused on extracting relationships described in single articles or in single sentences. However, a single record is rarely sufficient to judge upon the biological correctness of a relation, as experimental evidence might be weak or only valid in a certain context. Furthermore, statements may be more speculative than confirmative, and different articles often contradict each other. Experts therefore always take the complete literature into account to take a reliable decision upon a relationship. It is an open research question how to do this effectively in an automatic manner. Results We propose two novel relation extraction approaches which use recent representation learning techniques to create comprehensive models of biomedical entities or entity-pairs, respectively. These representations are learned by considering all publications from PubMed mentioning an entity or a pair. They are used as input for a neural network for classifying relations globally, i.e. the derived predictions are corpus-based, not sentence- or article based as in prior art. Experiments on the extraction of mutation–disease, drug–disease and drug–drug relationships show that the learned embeddings indeed capture semantic information of the entities under study and outperform traditional methods by 4–29% regarding F1 score. Availability and implementation Source codes are available at: https://github.com/mariosaenger/bio-re-with-entity-embeddings. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DLDTI: A learning-based framework for identification of drug-target interaction using neural networks and network representation

2020 ◽  
Author(s):  
Yihan Zhao ◽  
Kai Zheng ◽  
Baoyi Guan ◽  
Mengmeng Guo ◽  
Lei Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Drug Target ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Representation Learning ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Mapping Space ◽  
Network Representation ◽  
Low Dimensional

AbstractTo elucidate novel molecular mechanisms of known drugs, efficient and feasible computational methods for predicting potential drug-target interactions (DTI) would be of great importance. A novel calculation model called DLDTI was generated for predicting DTI based on network representation learning and convolutional neural networks. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning low-dimensional and rich depth features of drugs and proteins. Low-dimensional feature vectors were used to train DLDTI to obtain optimal mapping space and infer new DTIs by ranking DTI candidates based on their proximity to optimal mapping space. DLDTI achieves promising performance under 5-fold cross-validation with AUC values of 0.9172, which was higher than that of the method based on different classifiers or different feature combination technique. Moreover, biomedical experiments were also completed to validate DLDTI’s performance. Consistent with the predicted result, tetramethylpyrazine, a member of pyrazines, reduced atherosclerosis progression and inhibited signal transduction in platelets, via PI3K/Akt, cAMP and calcium signaling pathways. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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
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