1st International and 10th National Iranian Conference on Bioinformatics

Home Accepted Papers

Evaluation of COVID-19 mutations and predicting the rate of disease transmission and pathogenicity based on the types of mutations.

Paper ID : 1346-ICB10

Authors:

Tina Sadeh *

Abstract:

Researchers have developed a new method that uses Artificial Intelligence to foresee the most likely mutations of pathogens like SARS-COV-2, the virus that causes COVID-19. SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus [1]. As of Jan 5, already more than 3.15M people have been infected and 5.73M people died [2] RNA viruses are different than DNA-based viruses in the sense that they have higher mutation rates, and hence, they have higher adaptive capacity. This mutation causes continuous evolution that leads to host immunity and therefore, based on the type of mutation, it affects the rate of disease transmission and pathogenicity [3] This RNA virus can do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. The collected dataset is processed to determine the mutation of different parts of the Covid-19 separately[4]
We proposed a model for the Virus Mutations Prediction. The proposed approach consists of four main phases:
1. Sequences of datasets are preprocessed.
2. Once we have preprocessed sequences of data, they are transformed into a format that is suitable for training an LSTM network. In this case, a one-hot encoding of the integer values is used where each value is represented by a binary vector that is all “0” values except for the pointer to the word, which is set to 1
3. The input data are prepared to train on the LSTM encoder. After that, it is the role of the decoder to take the output from the encoder as integers and transform it into sequences
4. The obtained results are evaluated.

Keywords:

Artificial Intelligence, LSTM Algorithm, COVID-19, RNA viruses

Status : Paper Accepted (Poster Presentation)