NB model is a classification model based on Bayes' theorem and independent assumption of characteristic conditions. It requires few estimated parameters, is not sensitive to missing data, and the algorithm is relatively simple. In theory, the NB model has the smallest error rate compared with other classification methods.
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.