Synopsis
Understanding the host range of viruses is critical for controlling viral infections, designing antiviral therapies, and preventing zoonotic spillover events. Predicting the host specificity of viruses (e.g., bacteriophages, human viruses) based on their genomic sequences can provide insights into virus-host interactions and facilitate targeted interventions. In this project, we aim to develop a deep learning-based system to predict viral host range using genomic data. Sequence-based models like Convolutional Neural Networks (CNNs) or transformers will analyze viral genomes to identify features associated with host specificity. Additionally, Graph Neural Networks (GNNs) will model virus-host interactions as graphs, where nodes represent viruses and hosts, and edges represent infection relationships. This approach will enable rapid identification of viral hosts and improve our understanding of viral ecology.
Relevance of the Topic
Viruses are responsible for numerous diseases in humans, animals, and plants, and understanding their host range is essential for mitigating outbreaks and developing treatments. Automated prediction systems using deep learning can accelerate the identification of viral hosts, especially for emerging pathogens, and aid in the design of phage therapies for bacterial infections.
Future Research/Scope
- Zoonotic Spillover Prediction: Extend the model to predict the likelihood of zoonotic transmission based on viral genomic features.
- Phage Therapy Optimization: Use the model to identify bacteriophages with high specificity for pathogenic bacteria, aiding in phage therapy development.
- Cross-Species Transmission: Investigate viral mutations or genomic features that enable cross-species transmission.
- Explainability: Incorporate explainability techniques to identify key genomic regions driving host specificity predictions.
- Global Surveillance: Develop tools for large-scale surveillance of viral host ranges using genomic data from diverse geographic regions.
Skills Learned
- Deep Learning: Hands-on experience with CNNs, transformers, and GNNs for genomic and interaction data analysis.
- Bioinformatics: Understanding of viral genome annotation, sequence alignment, and feature extraction.
- Python Programming: Proficiency in Python and libraries like TensorFlow, PyTorch, and Biopython.
- Data Visualization: Skills in visualizing genomic and interaction data using tools like Matplotlib, Plotly, and Cytoscape.
Relevant courses to the topic
Reading List
- Books
- "Deep Learning for Biomedical Data Analysis" – Mourad Elloumi (Link)
- Research Papers
- "Bioinformatics approaches for unveiling virus-host interactions" – Iuchi et al., Computational and Structural Biotechnology Journal
Link - "HostNet: improved sequence representation in deep neural networks for virus-host prediction" – Ming et al., Bioinformatics
Link - "RNAVirHost: a machine learning–based method for predicting hosts of RNA viruses through viral genomes" – Cheng et al., Gigascience
Link - "Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning" – Shang et al., BMC Biology
Link - "VIDHOP, viral host prediction with deep learning" – Florian Mock et al., Bioinformatics
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- Datasets
- PHASTER (PHAge Search Tool Enhanced Release)
Link - NCBI Virus Database
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- Code Tutorials & Repositories
- How to build a machine learning model to predict antimicrobial peptides (End-to-end Bioinformatics) (YouTube/GitHub)
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- Videos & Playlists
- "Machine Learning in Computational Biology" – MIT
YouTube Playlist - "My Hero & Me - Different flavors of phage-host prediction powered by machine learning: how and why?" – YouTube Video
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