Synopsis
Probiotics are live microorganisms that confer health benefits to the host when administered in adequate amounts. Identifying potential probiotic strains and predicting their functional effects (e.g., immune modulation, metabolic health) is critical for developing next-generation probiotics and personalized microbiome therapies. In this project, we aim to use deep learning techniques to analyze bacterial genomes and predict probiotic properties such as the production of beneficial metabolites, adhesion to host cells, or resistance to stomach acid. Sequence-based models like Convolutional Neural Networks (CNNs) or transformers will be used to identify genomic features associated with probiotic functionality. Additionally, Graph Neural Networks (GNNs) will model interactions between probiotics and the host microbiome to predict functional outcomes. This approach will accelerate the discovery of novel probiotics and deepen our understanding of their mechanisms of action.
Relevance of the Topic
The human microbiome plays a crucial role in health and disease, and probiotics offer a promising avenue for modulating the microbiome to improve health outcomes. However, identifying effective probiotic strains and understanding their functional effects remain challenging. Automated prediction systems using deep learning can streamline probiotic discovery, reduce experimental costs, and enable personalized interventions based on individual microbiome profiles.
Future Research/Scope
- Functional Annotation: Extend the model to predict specific functional effects of probiotics, such as immune modulation, gut barrier enhancement, or metabolic regulation.
- Personalized Probiotics: Develop patient-specific models that recommend probiotics based on individual microbiome compositions.
- Interaction Modeling: Use GNNs to simulate complex interactions between probiotics, pathogens, and the host microbiome.
- Clinical Validation: Collaborate with experimental biologists to validate predicted probiotic strains in vitro or in vivo.
- Metagenomic Integration: Incorporate metagenomic data to predict how probiotics influence the overall microbiome composition.
Skills Learned
- Deep Learning: Hands-on experience with CNNs, transformers, and GNNs for genomic and microbiome data analysis.
- Bioinformatics: Understanding of bacterial 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 microbiome 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
- "Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art" – D'Urso et al., Applied Sciences
Link - "iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences" – Yu Sun et al., Briefings in Bioinformatics
Link
- Datasets
- Human Microbiome Project (HMP): Insights into the Role of Probiotics
Link - NCBI Genome: A Resource for Probiotic Strain Discovery
Link
- Videos & Playlists
