Genome Analysis of Nipah Virus Using Recurrent Neural Networks (RNN)

The Nipah virus, a highly pathogenic zoonotic virus, poses significant threats to human health, with high mortality rates and limited treatment options. Understanding its genomic structure is crucial for developing effective diagnostic and therapeutic strategies. This project leverages deep learning techniques, specifically Recurrent Neural Networks (RNNs), to analyze the Nipah virus genome. The primary objective is to identify key genetic features and variations that influence its pathogenicity. RNA sequence data of the virus is processed using Word2Vec for feature representation, transforming nucleotide sequences into vector embeddings. The trained RNN model is then employed to predict potential mutations in the viral genome and assess their implications for viral behavior. The proposed system aims to enhance our understanding of the virus’s genomic makeup, enabling more accurate predictions of pathogenic variations and facilitating the development of targeted interventions. Through this approach, we seek to contribute to the advancement of genomic analysis in viral pathogenesis and disease control.