Conditional random field based side-information fusion for distributed multi-view video coding

2011 ◽  
Author(s):  
Yongsheng Zhang ◽  
Hongkai Xiong ◽  
Hao Wang ◽  
Chang Wen Chen
Keyword(s):  
Video Coding ◽  
Random Field ◽  
Information Fusion ◽  
Side Information ◽  
Conditional Random Field

GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field

2021 ◽  
Author(s):  
Yongxian Fan ◽  
Meijun Chen ◽  
Xiaoyong Pan
Keyword(s):  
Random Field ◽  
Side Information ◽  
Conditional Random Field ◽  
Matrix Completion ◽  
Treatment Strategies ◽  
Prediction Method ◽  
Complementary Method ◽  
Regulatory Processes ◽  
Disease Associations ◽  
Benchmark Datasets

Abstract Long noncoding RNAs (lncRNAs) play important roles in various biological regulatory processes, and are closely related to the occurrence and development of diseases. Identifying lncRNA-disease associations is valuable for revealing the molecular mechanism of diseases and exploring treatment strategies. Thus, it is necessary to computationally predict lncRNA-disease associations as a complementary method for biological experiments. In this study, we proposed a novel prediction method GCRFLDA based on the graph convolutional matrix completion. GCRFLDA first constructed a graph using the available lncRNA-disease association information. Then, it constructed an encoder consisting of conditional random field and attention mechanism to learn efficient embeddings of nodes, and a decoder layer to score lncRNA-disease associations. In GCRFLDA, the Gaussian interaction profile kernels similarity and cosine similarity were fused as side information of lncRNA and disease nodes. Experimental results on four benchmark datasets show that GCRFLDA is superior to other existing methods. Moreover, we conducted case studies on four diseases and observed that 70 of 80 predicted associated lncRNAs were confirmed by the literature.

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Yan Yan ◽  
Faguo Zhou ◽  
Yifan Ge ◽  
Cheng Liu ◽  
Jingwu Feng
Keyword(s):  
Random Field ◽  
Mobile Applications ◽  
Conditional Random Field ◽  
User Reviews ◽  
Visualization System ◽  
Target Extraction

With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.

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