AI-Powered Multi-Omics Data Integration for Biomarker Discovery

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.

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.

• 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.

• 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.

• 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.