A novel ensemble of probabilistic neural network for predicting protein-protein interactions

2012 ◽  
Author(s):  
Yaou Zhao ◽  
Yuehui Chen ◽  
Mingyan Jiang
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Probabilistic Neural Network ◽  
Protein Protein Interactions

Auto Covariance Combined with Artificial Neural Network for Predicting Protein-Protein Interactions

2013 ◽  
Vol 765-767 ◽  
pp. 1622-1624
Author(s):  
Juan Juan Li ◽  
Yue Hui Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural ◽  
Biological Methods ◽  
Auto Covariance ◽  
Better Than

Proteins play biological function through the interactions in organisms. Proteins are major components of organisms, and they are of great significance. As an increasing number of high-throughput biological experiments are carried out, a large amount of biological data is produced. Bioinformatics is developed to study the relative data which turns out to be difficult to study using biological methods. The paper mainly studies how to apply the intelligent calculation methods to protein-protein interactions (PPIs) prediction. We proposed an approach, by combining auto covariance with artificial neural network classifier, to predict PPIs. Experiments show that our method performs better than related works with a 5% higher accuracy.

Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network

2017 ◽  
Vol 13 (7) ◽  
pp. 1336-1344 ◽  
Author(s):  
Yan-Bin Wang ◽  
Zhu-Hong You ◽  
Xiao Li ◽  
Tong-Hai Jiang ◽  
Xing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Deep Neural Network ◽  
Protein Sequences ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
P Protein ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Protein–protein interactions (PPIs) play an important role in most of the biological processes.

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