Description: The accessory gene content of a genome can be very important in determining the virulence, antimicrobial resistance, and metabolic capacity of a genome.  Although whole genome sequencing has become prominent in the past years, it does not always capture the entire genome due to sequencing errors, lack of depth, annotation errors, and etc.  Additionally, incomplete and partial genomes in the form of metagenomics are common.  Hence, the ability to predict the presence of accessory genes may be beneficial to researchers.  This talk will focus on using machine learning to predict the presence or absence of accessory genes in E. coli using a limited set of conserved genes.  Although it is not modeled on partial or metagenomic sequences specifically, this effort can be seen as a stepping stone to meet that goal. 

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