Diksha Gupta, RPTU Kaiserslautern-Landau

Title: RNN-based Mutation Rate Prediction for SARS-CoV-2

Abstract:

The COVID-19 pandemic has thrusted the world into an unprecedented crisis, prompting
widespread efforts to understand the dynamics of viral transmission, adaptation,
and evolution. One of the key challenges in combating the pandemic is the emergence of
novel variants of the SARS-CoV-2 virus, which can potentially affect the efficacy of vaccines,
diagnostics, and therapeutics. Predictive modeling of COVID-19 mutation rates
has therefore become a critical area of research, offering insights into the evolutionary
trajectory of the virus and informing public health strategies.
This thesis presents a comprehensive analysis of the predictive performance of Long
Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures in modeling
COVID-19 mutation rates. The primary objective of the study is to assess the efficacy
of these recurrent neural network models in capturing the complex patterns underlying
mutation dynamics and to evaluate their suitability for predictive tasks in bioinformatics.
The methodology involves collecting and preprocessing a diverse array of genomic
data, including viral genome sequences and associated metadata. Feature engineering
techniques are employed to represent genomic sequences in a format amenable to deep
learning models. LSTM and GRU architectures are then trained on the preprocessed
data to predict mutation rates associated with various genomic regions and mutation
types.
Through a series of experiments conducted on different mutation types and datasets,
the study systematically evaluates the performance of LSTM and GRU models. Performance
metrics such as root mean squared error, and mean absolute error are computed
to assess the models’ predictive capabilities. Additionally, visualization techniques are
employed to analyze the learned representations and gain insights into the underlying
mutation patterns. The findings of the study reveal the potential of LSTM and GRU
models in predicting COVID-19 mutation rates with high accuracy and precision.
Overall, this thesis contributes to the growing body of knowledge on computational
approaches to understanding viral evolution and offers valuable insights into the application
of deep learning models in COVID-19 research. By leveraging machine learning
techniques, researchers can gain a deeper understanding of the mechanisms driving viral
evolution and develop more effective strategies for combating the COVID-19 pandemic.