New Year Offer End Date: 30th April 2024
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Program Virtual Workshop

AI-Powered Multi-Omics Data Integration for Biomarker Discovery

Unlocking the Power of AI to Discover Novel Biomarkers through Multi-Omics Data Integration.

Skills you will gain:

About Workshop:

Multi-omics data, which includes genomics, transcriptomics, proteomics, and metabolomics, provides a comprehensive view of biological systems but often presents challenges in integration due to the scale and complexity of the data. This workshop focuses on how AI, particularly machine learning and deep learning, can streamline the integration process, offering new methods for identifying biomarkers. Through AI models, participants will learn how to process and analyze large-scale omics datasets to discover biomarkers linked to various diseases such as cancer, cardiovascular diseases, and neurodegenerative disorders.
Throughout the workshop, participants will gain hands-on experience with AI-driven tools for data integration and biomarker discovery. Case studies will demonstrate the real-world application of these AI technologies in precision medicine, showcasing how integrated data from various omics sources can lead to more effective diagnostics and personalized treatments.

Aim: This workshop aims to explore the integration of multi-omics data through AI technologies to uncover potential biomarkers for diseases. Participants will learn how machine learning can optimize the fusion of genomic, transcriptomic, proteomic, and metabolomic data, enabling more accurate biomarker identification for personalized medicine and improved disease diagnostics.

Workshop Objectives:

  • Learn the fundamentals of multi-omics data types (genomics, transcriptomics, proteomics, metabolomics).
  • Understand the applications of AI in integrating these omics datasets.
  • Gain experience using machine learning tools to analyze and integrate multi-omics data.
  • Explore how AI can aid in biomarker discovery for disease diagnostics.
  • Examine real-world case studies of AI in precision medicine and drug discovery.

What you will learn?

Day 1 – Introduction to Multi-Omics & AI Integration

  • Types of omics (genomics, transcriptomics, proteomics, metabolomics)
  • The challenge of data integration
  • AI algorithms for feature selection and dimensionality reduction
  • Case examples: Cancer and rare disease biomarker discovery

Day 2 – AI Tools for Multi-Omics Analysis

  • Data preprocessing across omics layers
  • ML/DL pipelines for integrative analysis (e.g., MOFA, DeepMOCCA)
  • Demonstration: Running a simple multi-omics integration workflow
  • Handling imbalanced datasets

Day 3 – Translational Applications & Validation

  • Clinical biomarker validation workflows
  • Using AI to predict therapeutic response
  • Regulatory and ethical considerations
  • Capstone Discussion: Designing an AI-based multi-omics biomarker pipeline

Mentor Profile

Professor & Dean Others
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Fee Plan

StudentINR 1799/- OR USD 70
Ph.D. Scholar / ResearcherINR 2799/- OR USD 80
Academician / FacultyINR 3799/- OR USD 95
Industry ProfessionalINR 4799/- OR USD 110

Important Dates

Registration Ends
15 Jan 2026 AT IST : 7:00 PM
Workshop Dates  15 Jan 2026 to 17 Jan 2026  AT IST : 8:00 PM

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Intended For :

  • Undergraduate/postgraduate degree in Bioinformatics, Biotechnology, Genomics, Molecular Biology, or related fields.
  • Professionals in healthcare, genomics, pharmaceuticals, biomedical research, and diagnostics.
  • Data scientists and AI/ML engineers interested in applying AI to multi-omics data analysis.
  • Individuals with a keen interest in biomarker discovery and its applications in personalized medicine.

Career Supporting Skills

Omics Data Integration Machine Learning for Healthcare Biomarker Discovery Data Science in Healthcare Precision Medicine Analytics Biomedical Data Processing

Workshop Outcomes

  • Ability to integrate and analyze multi-omics data using AI for biomarker discovery.
  • Proficiency in using machine learning algorithms for data analysis in healthcare research.
  • Real-world understanding of how AI-driven data integration enhances precision medicine.
  • Ability to identify biomarkers for diseases such as cancer and cardiovascular disorders.
  • Practical experience with AI tools and techniques for large-scale omics data analysis.