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

Introduction

Neural network computing, as the name suggests, is to simulate the operation mode of the computer to achieve the simulation of human brain neural autonomous judgment ability, adaptability, and the ability of multi-project integration and parallel processing. From a biological perspective, the human cerebral cortex contains more than 10 billion neurons connected to form a neural network. Information is collected through the body's sense of touch, taste and other sensory organs. The collected data is transmitted to the central nervous system and the connection points of the neural network. Then the data collected will be screened, sorted out and cleaned by the nerve center system, and then transmitted to the whole body according to different analysis results to coordinate the functions of various organs in the body.

Figure 1.

Neural network framework of inputs and functions to produce perceptron.

From a historical perspective, in the 1940s, McCulloch and Pitts discovered the work of turning logic devices into neurons (Palm, 1986); In the 1960s, Rosenblatt proposed the Perceptron model (Fig. 1), which utilizes simulated learning and recognition functions. It has several input terminals to represent the reception of signals from input or another perceptron. After summation of these signals, outputs or transmits them to other perceptrons through excitation functions. Nerve cells dendrites and axons, some close connection between neurons, some loose connection, and performance are high on the side of each connection weights on the perception of low, save the weight information is neural network acquisition of “knowledge”. However, due to the data dependence, use limitations and forced to end. Until recent years, modern high-tech approaches have provided a solid foundation for research into artificial intelligence, neural networks, deep learning, and other technologies, and the consumer market's expectation of intelligent devices has fueled researchers' enthusiasm for the development of neural networks and other related technologies (Admin, 2021).

Compared with the traditional computing methods, the deep neural network algorithm has the ability to self-adaptation and self-organization (Mahapatra, Gupta, Sahu, & Panda, 2021). The traditional computing methods rely on the knowledge and ability written by the programmers to the systematic program. Hence, it primarily depends on the early programmer to write the code, as well as the maintenance and update of the database. In this traditional model, the computing power and intelligence of the computer will be dramatically limited. They were looking at the new updated deep neuron network. The neural network algorithm provides machines with self-learning, self-identification and this specific ability can develop the computer beyond the capacity of its designer itself. Typically, the program designer sets up a learning method for the neural network algorithm or the content to imitate and periodically provides a sample standard for the computer to replicate. The other way is to let the algorithm learn on its own. In this case, the program designer only needs to build a learning instruction for the algorithm, and the algorithm will learn accordingly to the type and pattern of the imported data sets to achieve the goal of identifying the regularity and characteristics of the environment (Oja, 1994).

From the perspective of actual demand, in the 21st century, with the global outbreak of COVID-19 since late 2020, medical and health care has become the focus of exploring international research institutions. There is also a growing trend in understanding Protein-Protein Interactions (PPIs) around the world, such as the proteins that are the core building blocks of viruses. The PPI can provide series of exchanges of amino acids between protein molecules. After extracting the interactions between the molecules, the deep neuron network algorism can develop an ability to predict the future interactions in any given protein. That is to say, the core of a virus is a protein that interacts with each other to reproduce, which means that understanding these interactions could speed up efforts to defeat the virus.