Enabling Phenotypic AMR Prediction from NGS Data with Expert-Augmented Machine Learning and Curated Databases
Detecting and understanding antimicrobial resistance (AMR) is a critical challenge in modern microbiology, with far-reaching implications for public health and clinical decision-making. In a previous blog post, BugSeq’s Approach to AMR, we outlined the importance of combining genomic insights with machine learning (ML) to enhance AMR detection. However, not all computational approaches to AMR prediction enable the same accuracy, insight, and power. In this post, we discuss the advantages and limitations of different approaches and demonstrate how the approach BugSeq takes—expert-augmented machine learning combined with a curated AMR database—offers a more robust, interpretable, and powerful path for phenotypic AMR prediction (genomic AST).