  Biological datasets amenable to applied machine learning are more available
today than ever before, yet they lack adequate representation in the
Data-for-Good community. Here we present a work in progress case study
performing analysis on antimicrobial resistance (AMR) using standard ensemble
machine learning techniques and note the successes and pitfalls such work
entails. Broadly, applied machine learning (AML) techniques are well suited to
AMR, with classification accuracies ranging from mid-90% to low- 80% depending
on sample size. Additionally, these techniques prove successful at identifying
gene regions known to be associated with the AMR phenotype. We believe that the
extensive amount of biological data available, the plethora of problems
presented, and the global impact of such work merits the consideration of the
Data- for-Good community.
