Machine Learning Approaches for High‐Throughput Microbial Identification/Culturing

The advancement of high-throughput technologies at a high pace has transformed microbial research via the prospect of large-scale microorganism cultivation and identification. Despite such technological progress, traditional microbial methods remain limited in sensitivity, scalability, and in culturing a significant proportion of environmental microbiota. This chapter elaborates on the ways in which artificial intelligence and machine learning are revolutionizing the discipline of microbial identification and culturing by being integrated with HTP platforms. The application of supervised, unsupervised, and reinforcement learning (RL) paradigms in microbial species detection, antimicrobial resistance (AMR) prediction, and phenotypic profiling is elaborated. Core ML models, ranging from support vector machines to deep neural networks, are assessed for a variety of domains, including metagenomics, proteomics, and synthetic biology, as discussed in subsequent chapters of this book. Overall, this chapter offers a general overview of the way ML technologies are revolutionizing HTP microbial science, ushering in the potential for next-generation diagnostics, ecological surveys, and biotechnological innovation.