By assuming that the products have specific attributes, the actual effects are simulated, and then consumers are allowed to evaluate these virtual products according to their preferences. In addition, these characteristics are separated from the utility of attribute level by the mathematical statistics method so that the importance of each attribute and attribute level can be quantitatively evaluated.
Published in Chapter:
Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)
Zizhe Gao (Columbia University, USA) and Hao Lin (Northeastern University, USA)
Copyright: © 2022
|Pages: 23
DOI: 10.4018/978-1-7998-8455-2.ch006
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.