Machine Learning for Oligonucleotide Analysis in Practice

Recent advances in machine learning have significantly improved the ability to evaluate data quality from separation techniques used in oligonucleotide analysis. These models have helped identify subtle patterns and anomalies that traditional methods may overlook, leading to more precise and reliable outcomes. Professor Fornstedt talks about his experiences of using machine learning to aid the analysis of oligonucleotides. Watch the full video above.