Biomarkers 2.0: Predictive Power with AI

Aim

To equip PhD scholars and academicians with advanced skills in AI-driven biomarker discovery. This workshop focuses on the role of AI in identifying predictive biomarkers for complex diseases such as cancer, cardiovascular, and neurological disorders, emphasizing emerging research trends, AI models, and ethical implications.

Workshop Objectives

• Understand the integration of AI in biomarker discovery.
• Analyze AI models for predicting complex diseases.
• Learn model validation techniques for AI-driven biomarkers.
• Address ethical and data challenges in AI biomarker research.
• Explore AI applications in precision medicine and translational research.

Workshop Structure

Module 1: Advanced Introduction to AI-Driven Biomarkers

• Introduction to Biomarkers: Classical Methods vs. AI Integration
• Overview of traditional biomarker discovery methods
• Introduction to AI’s role in transforming biomarker discovery
• Historical perspective of biomarker discovery
• The rise of AI in predictive biomarker development

Module 2: AI’s Role in Predictive Biomarkers for Complex Diseases

• Theoretical exploration of machine learning (ML) and deep learning (DL) techniques
• Case studies on AI-based biomarkers in complex diseases (Cancer, Cardiovascular, Neurological)
• Journal reviews on AI-driven biomarkers
• Case studies from recent research in the field

Module 3: Deep Dive into AI Models for Biomarker Identification

• Accuracy, precision, and generalizability of AI in biomarker discovery
• Theoretical exploration of validation techniques
• Large-scale omics data handling
• AI’s role in data preprocessing, feature selection, and overcoming challenges

Module 4: Data Management, Model Interpretation, and Ethical Considerations

• AI’s role in minimizing overfitting and bias
• Challenges in interpretability and transparency in biomarker models
• Theoretical frameworks on ethical and legal considerations
• Responsible use of AI in healthcare biomarker research

Module 5: Future Directions in AI-Enhanced Biomarker Research

• The role of AI in the development of precision medicine biomarkers
• Translational research and its importance in healthcare
• AI’s contribution to systems biology and biomarker discovery

Participant’s Eligibility

AI researchers, bioinformaticians, medical researchers, healthcare professionals, and academic scholars.

Workshop Outcomes

• Master AI techniques for identifying predictive biomarkers.
• Learn to apply ML and DL models in biomarker research.
• Handle and preprocess large-scale biological datasets.
• Explore case studies in cancer, cardiovascular, and neurological research.
• Address ethical challenges and apply AI models responsibly.