Machine Learning for High Throughput DNA-Aptamer Screening and Selection

Explore how machine learning techniques can revolutionize DNA-aptamer screening and selection processes in this 29-minute conference talk by Andrea Bertozzi from UCLA, presented at IPAM's Mathematics of Cancer workshop. Discover the intersection of computational methods and molecular biology as Bertozzi demonstrates how advanced algorithms can accelerate the identification and optimization of DNA aptamers - short DNA sequences that bind specifically to target molecules. Learn about high-throughput screening methodologies, the challenges of processing large datasets in aptamer research, and how machine learning approaches can improve selection efficiency and accuracy. Gain insights into the mathematical frameworks underlying these screening processes and their potential applications in cancer research and therapeutic development. Understand the computational strategies for analyzing binding affinity data, pattern recognition in molecular interactions, and the optimization of aptamer libraries for specific biological targets.