Biomarkers 2.0: Predictive Power With Ai

What you’ll learn
Advanced Introduction to AI-Driven Biomarkers
AI’s Role in Predictive Biomarkers for Complex Diseases
Deep Dive into AI Models for Biomarker Identification
Data Management, Model Interpretation, and Ethical Considerations
Future Directions in AI-Enhanced Biomarker Research

Requirements
No prior experience in AI or biomarker research is necessary to enroll in this workshop. We welcome participants from diverse backgrounds, including those new to the field. This workshop is designed to provide a supportive learning environment where foundational concepts will be introduced and built upon throughout the course. Join us to enhance your understanding and skills in this exciting and rapidly evolving area of research!

Description
AimTo 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 ObjectivesUnderstand 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 StructureModule 1: Advanced Introduction to AI-Driven BiomarkersIntroduction to Biomarkers: Classical Methods vs. AI IntegrationOverview of traditional biomarker discovery methodsIntroduction to AI’s role in transforming biomarker discoveryHistorical perspective of biomarker discoveryThe rise of AI in predictive biomarker developmentModule 2: AI’s Role in Predictive Biomarkers for Complex DiseasesTheoretical exploration of machine learning (ML) and deep learning (DL) techniquesCase studies on AI-based biomarkers in complex diseases (Cancer, Cardiovascular, Neurological)Journal reviews on AI-driven biomarkersCase studies from recent research in the fieldModule 3: Deep Dive into AI Models for Biomarker IdentificationAccuracy, precision, and generalizability of AI in biomarker discoveryTheoretical exploration of validation techniquesLarge-scale omics data handlingAI’s role in data preprocessing, feature selection, and overcoming challengesModule 4: Data Management, Model Interpretation, and Ethical ConsiderationsAI’s role in minimizing overfitting and biasChallenges in interpretability and transparency in biomarker modelsTheoretical frameworks on ethical and legal considerationsResponsible use of AI in healthcare biomarker researchModule 5: Future Directions in AI-Enhanced Biomarker ResearchThe role of AI in the development of precision medicine biomarkersTranslational research and its importance in healthcareAI’s contribution to systems biology and biomarker discoveryParticipant’s EligibilityAI researchers, bioinformaticians, medical researchers, healthcare professionals, and academic scholars.Workshop OutcomesMaster 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.

Overview
Section 1: Introduction

Lecture 1 Advanced Introduction to AI-Driven Biomarkers

Lecture 2 AI’s Role in Predictive Biomarkers for Complex Diseases

Lecture 3 Deep Dive into AI Models for Biomarker Identification

Lecture 4 Data Management, Model Interpretation, and Ethical Considerations

Lecture 5 Future Directions in AI-Enhanced Biomarker Research

Lecture 6 Ethical & Legal Challenges Module

Lecture 7 Future Directions in AI Module

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