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Using Deep Neural Networks to Improve the Performance of Protein–Protein Interactions Prediction
- Source :
- International Journal of Pattern Recognition and Artificial Intelligence. 34:2052012
- Publication Year :
- 2020
- Publisher :
- World Scientific Pub Co Pte Lt, 2020.
-
Abstract
- Protein–protein interactions (PPIs) help to elucidate the molecular mechanisms of life activities and have a certain role in promoting disease treatment and new drug development. With the advent of the proteomics era, some PPIs prediction methods have emerged. However, the performances of these PPIs prediction methods still need to be optimized and improved. In order to optimize the performance of the PPIs prediction methods, we used the dropout method to reduce over-fitting by deep neural networks (DNNs), and combined with three types of feature extraction methods, conjoint triad (CT), auto covariance (AC) and local descriptor (LD), to build DNN models based on amino acid sequences. The results showed that the accuracy of the CT, AC and LD increased from 97.11% to 98.12%, 96.84% to 98.17%, and 95.30% to 95.60%, respectively. The loss values of the CT, AC and LD decreased from 27.47% to 14.96%, 65.91% to 17.82% and 36.23% to 15.34%, respectively. Experimental results show that dropout can optimize the performances of the DNN models. The results can provide a resource for scholars in future studies involving the prediction of PPIs. The experimental code is available at https://github.com/smalltalkman/hppi-tensorflow .
- Subjects :
- 0303 health sciences
business.industry
Computer science
02 engineering and technology
Computational biology
Protein–protein interaction
03 medical and health sciences
Drug development
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Deep neural networks
020201 artificial intelligence & image processing
Protein–protein interaction prediction
Computer Vision and Pattern Recognition
Artificial intelligence
business
Software
Dropout (neural networks)
Disease treatment
030304 developmental biology
Subjects
Details
- ISSN :
- 17936381 and 02180014
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- International Journal of Pattern Recognition and Artificial Intelligence
- Accession number :
- edsair.doi...........1665bc203d528e71f85ed93a6d40e89c
- Full Text :
- https://doi.org/10.1142/s0218001420520126