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

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.

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.


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