基于深度神经网络(DNN)的蛋白质-蛋白质相互作用(PPI)

Zizhe Gao, Hao Lin
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引用次数: 0

摘要

进入21世纪,计算机科学与生物学的研究进入了一个快速发展的阶段。随着资本快速流入重大健康研究领域,大量学者和投资者开始关注神经网络科学对生物识别的影响,特别是生物相互作用的研究。随着计算机技术的飞速发展,科学家们不断改进或完善传统的实验方法。本章旨在证明Satyajit Mahapatra和Ivek Raj Gupta项目团队开发的方法和计算算法的可靠性。在本章中,三个数据集负责验证计算算法,它们是s.c reevisiae, h.p ylori和Human-B。炭疽。在这三组数据中,葡萄球菌是核心子集。结果表明,这三种数据集的准确率分别为87%、87.5%和89%,精度分别为87%、86%和87%。
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Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)
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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