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Mutation Prediction of Infectious Viruses Based on Different Machine Learning Approaches

Ehteshami, Khashayar | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 56256 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Ghafourian Ghahramani, Amir Ali; Kavousi, Kaveh
  7. Abstract:
  8. Predicting the evolution of viruses is vital in controlling, preventing, and treating diseases. Mutations that evade the host immune system can propagate and persist through generations, making it crucial to anticipate and combat them effectively. The 1918 H1N1 pandemic serves as an example of the devastating impact of pandemics caused by viral mutations. By predicting mutations in advance, we can identify potential future pandemics and take effective preventative measures to mitigate their impact. Proteins play a vital role in the functioning of viruses. They are involved in various processes, such as replication, transcription, and host cell invasion. Any changes in the protein sequence due to mutations can alter its properties, including its interaction with the host immune system, making it important to predict mutations in advance. We utilize machine learning methods to generate protein sequences as our approach to predicting mutations. Our method combines Generative Adversarial Networks (GANs) and Sequence-to-Sequence (Seq2seq) networks, utilizing Long Short-Term Memories (LSTMs) to generate sequences. We use a discriminator to distinguish between fake and real sequences. Despite the challenges of GANs not being optimized for sequential data and Seq2seq networks having issues with longer-length sequences, we were able to generate high-quality sequences with BLOSUM score of 980 and Levenshtein distance of 5 against our test data
  9. Keywords:
  10. Machine Learning ; Generative Adversarial Networks ; Long Short Term Memory (LSTM) ; Protein Sequence ; Sequence-to-Sequence Network ; Infectious Diseases

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