Course Content

Abstract :

In this present time we are worrying about covid which is creating huge distruction .the way we can stop this spread is as soon as we bring the vaccine for it.so we need to study about the structure of B-cells. B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. They can inhibit their functioning by binding antibodies to antigen proteins. Predicting of epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. They can inhibit their functioning by binding antibodies to antigen proteins. Predicting of epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Decision Tree and Random Forest to predict which epitope can be used for vaccine production based on user entered input parameters of the epitope in the front end.

 

 

 

 

Introduction:

As we all know, the coronavirus has stopped the movements of the entire world. +is virus is so deadly that it is taking lives of the more than thousands of people every day and affecting millions of people on the globe. However, the disease was first reported in the Wuhan city of China, where the virus was isolated from a patient with respiratory symptoms in December 2019 later identified it by the name of COVID-19 . +e World Health Organization (WHO) announced this disease as a pandemic disease that spread from China to more than a hundred countries in the world. +e disease had already struck more than million persons of whom thousands of peoples died from COVID19 infection and the majority of them were reported from China, Italy, the United State of America, Britain, and Spain. Coronaviruses are the large group of viruses belonging to the family Coronaviridae and the order Nidovirales that are common among animals . +e Coronaviridae family is divided into four genera based on their genetic properties, including alpha, beta, gamma, and delta coronavirus genus . +e 2019-nCoV is an enveloped positive-sense RNA, beta coronavirus with a genome of 29.9 kb . +ey are zoonotic, transmitted from animals to humans . COVID19 affects the respiratory system (lungs and breathing tubes). Most COVID-19 patients developed severe acute respiratory illness with symptoms of fever, cough, and shortness of breath. Maximum reported cases of COVID-19 have been linked through travel to or residence in countries in this region . Presently, there are no clinically approved vaccines available in the world for this disease. +e development of a new vaccine for this new emergent strain by using therapeutic and preventive approach can be readily applied to save human lives. +e use of peptides or epitopes as therapeutics is a good strategy as it has advances in design, stability, and delivery . Moreover, there is a growing importance on the use of peptides in vaccine design by predicting immunogenic CTL, HTL, and B-cell epitopes from tissue-specific proteins of the structural proteins of SARS-CoV-2, the CoV envelope (E) protein is a small integral membrane protein involved in several aspects of the life cycle of the virus, such as envelope formation, assembly, budding, and pathogenesis. +us, it is considered to be a promising target for effective COVID19 vaccine design. More importantly, T-cell-based cellular immunity is essential for cleaning SARS-CoV-2 infection because it is memory based. +e E protein is a highly conserved protein having very low mutation rate. +is protein can elicit both cellular immunity, and neutralizing antibody against COVID-19 is necessary for efficient vaccine development.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Decision Tree and Random Forest to predict which epitope can be used for vaccine production based on user entered input parameters of the epitope in the front end.

 

 

 

 

 

 

 

 

 

Objective:

The main aim of this project to predict the covid-19 epitope using machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest, Decision Tree and Naïve Bayes based on different epitope parameters entered by the user in the front end.

 

Problem Statement

In order to accelerate the process of COVID-19 vaccination development, we need to identify the antigen. The antigen can predicted the epitope region which is a part of the antigen molecule. . Predicting of epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production.

.

 

 

 

Proposed System:

 

We proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Decision Tree and Random Forest to predict which epitope can be used for vaccine production based on user entered input parameters of the epitope in the front end.