Abstract
Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated biologists. Microbes play a crucial role in the material and elemental cycles of the natural world. Growing own microbes for research purposes requires a significant time and financial investment. On the other hand, high-throughput sequencing technology cannot advance at the same clip as the culture method. The area of microbiology has made substantial use of machine learning (ML) methods to tackle this problem.
Classification and prediction have emerged as key avenues for advancing microbial community research in computational biology. This research compares the advantages and disadvantages of using different algorithmic approaches in four subfields of microbiology (pathogen and epidemiology; microbial ecology; drug development; microbiome and taxonomy).