Microbiome-Based Precision Medicine: Multi-Omics, AI & Next-Generation Probiotics
Decode Microbial Intelligence Using AI, Multi-Omics & Data-Driven Approaches for Personalized Health and Next-Generation Therapeutics
Skills you will gain:
About Program:
This workshop introduces participants to modern computational approaches for analyzing microbiome data, integrating microbiome–metabolome interactions, identifying microbial biomarkers, and applying machine learning models for precision microbiome applications.
Through demonstrations and hands-on activities, participants will explore workflows involving 16S rRNA analysis, functional prediction, microbiome biomarker discovery, AI-based classification, explainable machine learning, and next-generation probiotic candidate prioritization.
Aim: This workshop aims to provide participants with an advanced understanding of microbiome-based precision medicine by integrating microbiome profiling, multi-omics analysis, artificial intelligence, and biomarker discovery approaches. Participants will learn how microbial communities can be analyzed and translated into personalized interventions, including next-generation probiotic strategies.
Program Objectives:
- Understand the role of microbiomes in precision medicine and personalized healthcare
- Analyze microbiome sequencing data for taxonomic and functional interpretation
- Explore microbiome diversity, microbial signatures, and disease-associated patterns
- Understand microbiome–metabolite interactions through multi-omics integration
- Apply artificial intelligence and machine learning approaches for biomarker discovery
- Perform feature selection and interpret ML models using explainable AI approaches
- Identify potential next-generation probiotic candidates based on microbial functions and evidence
- Develop a workflow connecting microbiome data with personalized intervention strategies
What you will learn?
Day 1 – Microbiome Profiling & Functional Interpretation
- Precision microbiome medicine: concepts and applications
- 16S rRNA and metagenomic microbiome data
- Microbiome diversity, taxonomy and microbial signatures
- Introduction to QIIME2-based microbiome analysis
- Functional prediction using PICRUSt2
- Linking microbial taxa with biological functions and host health
Hands-On:
Analyze a prepared 16S microbiome dataset to identify differential microbial taxa and predict functional pathways using QIIME2/PICRUSt2 outputs.
Day 2 – Multi-Omics Integration & AI-Based Biomarker Discovery
- Integrating microbiome, metabolomics and clinical metadata
- Microbiome–metabolite associations
- Data preprocessing and feature selection
- Machine learning for microbiome biomarker discovery
- Random Forest-based classification
- Feature importance and SHAP-based interpretation
- Identifying microbial and metabolic biomarker signatures
Hands-On:
Build a machine-learning model using microbiome + metabolomics features to classify sample groups and rank potential biomarkers.
Day 3 – Next-Generation Probiotics & Precision Intervention
- Conventional vs next-generation probiotics
- Selecting candidate probiotic strains from microbiome evidence
- Functional traits and metabolite-based strain evaluation
- Prebiotics, synbiotics and microbial consortia
- AI-assisted probiotic candidate prioritization
- Patient stratification and personalized microbiome intervention
- Translating microbiome biomarkers into precision therapeutic strategies
Hands-On:
Develop a precision-probiotic selection workflow by ranking candidate strains using microbial abundance, functional pathways, metabolite associations and predicted response.
Key Tools
QIIME2 • PICRUSt2 • Python • Google Colab • pandas • scikit-learn • SHAP
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Students, researchers, and professionals from Biotechnology, Microbiology, Bioinformatics, Biomedical Sciences, Genetics, Life Sciences, and Computational Biology
- PhD scholars, postdocs, faculty members, and industry professionals working in microbiome, multi-omics, AI, and precision medicine
- Data scientists and AI enthusiasts interested in biological applications
-
Career Supporting Skills
Program Outcomes
✅ Interpret microbiome sequencing datasets and analyze microbial community composition
✅ Perform microbial diversity analysis and identify important taxonomic signatures
✅ Understand functional pathways and biological roles associated with microbial populations
✅ Integrate microbiome, metabolomics, and clinical datasets for multi-omics insights
✅ Apply machine learning approaches for microbiome-based classification and biomarker discovery
✅ Interpret AI-driven microbial features using explainable AI (XAI) methods
✅ Identify and evaluate potential next-generation probiotic candidates using computational approaches
✅ Understand microbiome–host interactions and their role in precision medicine applications
✅ Design a basic end-to-end precision microbiome analysis workflow for research and therapeutic applications