This 1-2 hour workshop introduces participants to AI’s transformative role in biomarker discovery, highlighting machine learning and deep learning techniques, case studies in healthcare, and model validation methods. The workshop also covers the challenges in handling large-scale biological data and ethical considerations in AI-driven research.

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.

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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
1. Introduction to Biomarkers: Classical Methods vs. AI Integration
  - Overview of traditional biomarker discovery methods
  - Introduction to AI’s role in transforming biomarker discovery
2. Evolution of 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
1. AI Models in Biomarker Discovery
  - Theoretical exploration of machine learning (ML) and deep learning (DL) techniques
  - Case studies on AI-based biomarkers in complex diseases (Cancer, Cardiovascular, Neurological)
2. Cutting-Edge Research Trends in AI Biomarker Discovery
  - Journal reviews on AI-driven biomarkers
  - Case studies from recent research in the field
Module 3: Deep Dive into AI Models for Biomarker Identification
1. AI Model Validation Techniques
  - Accuracy, precision, and generalizability of AI in biomarker discovery
  - Theoretical exploration of validation techniques
2. Data Challenges in Biomarker Research
  - 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
1. Addressing Model Challenges: Overfitting, Bias, and Interpretability
  - AI’s role in minimizing overfitting and bias
  - Challenges in interpretability and transparency in biomarker models
2. Ethical and Legal Challenges in AI-Driven Biomarker Research
  - Theoretical frameworks on ethical and legal considerations
  - Responsible use of AI in healthcare biomarker research
Module 5: Future Directions in AI-Enhanced Biomarker Research
1. Predictive Biomarkers for Precision Medicine
  - The role of AI in the development of precision medicine biomarkers
  - Translational research and its importance in healthcare
2. Emerging Trends in AI-Driven Systems Biology
  - 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.

Fee Structure

Student

INR. 1000
USD. 25

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Access to Live Lectures
Access to Recorded Sessions
e-Certificate
Query Solving Post Workshop

Future Career Prospects

AI Research Scientist in Healthcare
Biomarker Analyst
Precision Medicine Consultant
Bioinformatics Specialist
AI in Biomedical Researcher
Healthcare Data Scientist

Job Opportunities

Pharmaceutical companies
Research institutions
Healthcare technology startups
Bioinformatics firms
Hospitals focusing on precision medicine
Government and regulatory bodies in healthcare

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