Machine Learning for Functional Protein Design - Class 1

Overview

Dive into the fundamentals of machine learning applications for functional protein design in this introductory lecture by Helder Ribeiro, exploring key concepts and methodologies in biological physics.

Syllabus

• Introduction to Machine Learning
Overview of Machine Learning (ML) and its applications
Distinction between supervised, unsupervised, and reinforcement learning
• Basics of Protein Structure and Function
Introduction to proteins and their role in biological systems
Structure-function relationship in proteins
• Machine Learning Applications in Biological Physics
Historical perspective of ML in biological sciences
Key achievements in ML for protein design
• Essential Machine Learning Concepts for Protein Design
Data representation and feature extraction for biological data
Introduction to model training and evaluation
• Understanding Functional Protein Design
Definition and significance of functional protein design
Case studies of successful functional protein designs using ML
• Introduction to Key Methodologies
Neural networks and deep learning for protein structure prediction
Genomic sequence analysis using ML techniques
• Practical Considerations
Overview of data sources and databases for protein research
Ethical considerations and challenges in computational biology
• Conclusions and Future Directions
Emerging trends in machine learning applications for protein design
Q&A and discussion of participant insights and potential applications

Subjects

Computer Science