AI-Powered Metabolomics & Precision Medicine: Disease Biomarker Discovery, Multi-Omics Analysis & Machine Learning
From Metabolite Profiles to Precision Medicine — Use AI and Multi-Omics to Discover Disease Biomarkers and Molecular Signatures.
Skills you will gain:
About Workshop:
This 3-day mentor-led workshop introduces participants to AI-assisted metabolomics and precision medicine, focusing on metabolite data analysis, disease biomarker discovery, multi-omics integration, and machine-learning-based prediction. Participants will learn how to process and interpret metabolomics datasets, identify disease-associated metabolic signatures, integrate metabolomic information with genomic and transcriptomic data, and develop interpretable predictive models for research and precision-health applications.
Aim: To provide practical training in metabolomics data analysis, biomarker discovery, multi-omics integration, and AI/ML, enabling participants to identify disease-associated metabolic signatures and develop computational workflows for precision medicine research.
Workshop Objectives:
- Understand the principles and applications of metabolomics in biomedical research.
- Explore metabolite datasets and common metabolomics data structures.
- Perform metabolomics data preprocessing, normalization, and quality assessment.
- Identify differentially abundant metabolites between biological conditions.
- Apply statistical and multivariate methods for metabolite profiling.
- Visualize metabolic patterns using PCA, heatmaps, volcano plots, and related approaches.
- Perform pathway and metabolite-set enrichment analysis.
- Identify candidate disease biomarkers using statistical and machine-learning approaches.
- Integrate metabolomics with genomics and transcriptomics datasets.
- Build machine-learning models for disease classification and biomarker prediction.
- Evaluate model performance and avoid common sources of overfitting.
- Apply explainable AI to understand important metabolic features.
- Develop interpretable biomarker signatures for precision-medicine research.
What you will learn?
📅 Day 1: Metabolomics Data Analysis & Disease-Associated Metabolic Signatures
Focus: Process metabolomics datasets and identify metabolic differences associated with disease or biological conditions.
Topics Covered
- Introduction to metabolomics and its role in precision medicine
- Targeted vs untargeted metabolomics
- Metabolite data formats and experimental design
- Data quality control, missing-value handling, normalization, and transformation
- Exploratory data analysis and metabolite distributions
- Univariate statistical analysis and differential metabolite analysis
- Multivariate analysis and dimensionality reduction
- PCA and clustering for metabolic-pattern discovery
- Heatmaps, volcano plots, and metabolite-profile visualization
- Metabolite annotation and biological interpretation
🛠️ Hands-on
Analyse a curated metabolomics dataset, preprocess the data, identify significantly altered metabolites, and visualize disease-associated metabolic patterns.
Workflow:
Metabolomics Data → QC → Normalization → Statistical Analysis → PCA/Clustering → Metabolic Signatures
🧰 Tools Covered
Python, R, pandas, NumPy, scikit-learn, MetaboAnalyst, matplotlib, ggplot2, Google Colab
📅 Day 2: Biomarker Discovery & Metabolic Pathway Analysis
Focus: Identify candidate biomarkers and interpret their biological relevance through pathway-level analysis.
Topics Covered
- Principles of metabolomics-based biomarker discovery
- Selection and prioritization of candidate metabolites
- Biomarker performance and diagnostic classification concepts
- ROC curves, AUC, sensitivity, and specificity
- Feature selection and dimensionality reduction
- Metabolite correlation and metabolic-network analysis
- Pathway enrichment and metabolite-set enrichment
- Metabolic pathway interpretation
- Disease-associated metabolic signatures
- Integration of metabolite and biological pathway information
🛠️ Hands-on
Prioritize candidate biomarkers, evaluate their discriminatory performance, perform pathway analysis, and construct a disease-associated metabolic signature.
Workflow:
Differential Metabolites → Feature Selection → Biomarker Evaluation → Pathway Analysis → Biological Interpretation
🧰 Tools Covered
MetaboAnalyst, Python, R, scikit-learn, pandas, Plotly, pathway/metabolite databases, Google Colab
📅 Day 3: Multi-Omics Integration & AI-Based Precision Medicine
Focus: Integrate metabolomics with other molecular datasets and develop interpretable ML models for disease prediction.
Topics Covered
- Principles of multi-omics integration
- Integration of metabolomics with genomics and transcriptomics
- Correlation-based and network-based multi-omics analysis
- Identification of cross-omics molecular signatures
- Machine-learning approaches for disease classification
- Random Forest and XGBoost-based prediction
- Feature engineering and feature selection
- Model training, validation, and cross-validation
- Evaluation using accuracy, precision, recall, F1-score, ROC-AUC, and related metrics
- Explainable AI and feature-importance analysis
- SHAP-based interpretation of metabolic biomarkers
- Biomarker-panel development and precision-medicine applications
🛠️ Hands-on
Integrate multi-omics datasets, train an ML disease-classification model, identify important metabolites and molecular features, and develop an interpretable biomarker signature.
Workflow:
Multi-Omics Data → Integration → Feature Engineering → ML Prediction → XAI → Biomarker Panel → Precision-Medicine Insights
🧰 Tools Covered
Python, R, pandas, scikit-learn, XGBoost, SHAP, Cytoscape, Plotly, Google Colab
Mentor Profile
Fee Plan
Important Dates
12 Nov 2026 Indian Standard Timing 4:30 PM
12 Nov 2026 to 14 Nov 2026 Indian Standard Timing 5:30 PM
Get an e-Certificate of Participation!
Intended For :
Students
- Graduate and postgraduate students in Biotechnology, Bioinformatics, Biochemistry, Chemistry, Pharmacy, Life Sciences, Biomedical Sciences, Genetics, Microbiology, and related disciplines.
PhD Scholars & Researchers
- Researchers working in metabolomics, systems biology, molecular biology, computational biology, biomarker discovery, precision medicine, and biomedical research.
Academicians
- Faculty members and researchers interested in applying AI, machine learning, and multi-omics approaches to biomedical research.
Industry Professionals
- Professionals from pharmaceutical R&D, biotechnology, diagnostics, healthcare analytics, precision medicine, biomarker development, and computational drug discovery.
Career Supporting Skills
Workshop Outcomes
- Explain the role of metabolomics in disease research and precision medicine.
- Process, normalize, and quality-control metabolomics datasets.
- Identify disease-associated metabolites and metabolic signatures.
- Perform statistical and multivariate metabolomics analysis.
- Apply PCA, clustering, and visualization techniques to metabolite data.
- Perform pathway and metabolite-set enrichment analysis.
- Identify and prioritize candidate disease biomarkers.
- Evaluate biomarker performance using ROC-AUC and related metrics.
- Integrate metabolomics with genomic and transcriptomic information.
- Build and validate ML models for disease classification.
- Apply XGBoost, Random Forest, and explainable-AI approaches.
- Identify important metabolic features using SHAP.