Event Information:

• Fri

  27

  Sep

  2024

  SC Seminar: Diksha Gupta

  11:00Room 42-260

  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.