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Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)

Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)

Zizhe Gao, Hao Lin
Copyright: © 2022 |Pages: 23
ISBN13: 9781799884552|ISBN10: 1799884554|ISBN13 Softcover: 9781799884569|EISBN13: 9781799884576
DOI: 10.4018/978-1-7998-8455-2.ch006
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MLA

Gao, Zizhe, and Hao Lin. "Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)." Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, edited by Richard S. Segall and Gao Niu, IGI Global, 2022, pp. 154-176. https://doi.org/10.4018/978-1-7998-8455-2.ch006

APA

Gao, Z. & Lin, H. (2022). Protein-Protein Interactions (PPI) via Deep Neural Network (DNN). In R. Segall & G. Niu (Eds.), Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning (pp. 154-176). IGI Global. https://doi.org/10.4018/978-1-7998-8455-2.ch006

Chicago

Gao, Zizhe, and Hao Lin. "Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)." In Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, edited by Richard S. Segall and Gao Niu, 154-176. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8455-2.ch006

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Abstract

Entering the 21st century, computer science and biological research have entered a stage of rapid development. With the rapid inflow of capital into the field of significant health research, a large number of scholars and investors have begun to focus on the impact of neural network science on biometrics, especially the study of biological interactions. With the rapid development of computer technology, scientists improve or perfect traditional experimental methods. This chapter aims to prove the reliability of the methodology and computing algorithms developed by Satyajit Mahapatra and Ivek Raj Gupta's project team. In this chapter, three datasets take the responsibility to testify the computing algorithms, and they are S. cerevisiae, H. pylori, and Human-B. Anthracis. Among these three sets of data, the S. cerevisiae is the core subset. The result shows 87%, 87.5%, and 89% accuracy and 87%, 86%, and 87% precision for these three data sets, respectively.

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