In this paper, we explore a representation methodology for the compression of DNA isolates. Using lossless string compression via tokenization of frequently repeated segments of DNA, we reduce the length of the isolates to be counted as k-mers for classification. With this new representation, we apply a previously established feature sampling method to dramatically reduce the feature space. In understanding the genetic diversity, we also look at conserving biological function across these spaces. Using a random forest model we were able to predict the resistance or susceptibility of bacteria with 85-90\% accuracy, with a 30-50\% reduction in overall isolate length, and an 80-90\% reduction in the feature space over baseline. Significant contributions were built upon previous analysis of similar data.
Partee, John; Hazell, Robert; Solsi, Anjli; and Santerre, John
"Compressed DNA Representation for Efficient AMR Classification,"
SMU Data Science Review: Vol. 3:
2, Article 5.
Available at: https://scholar.smu.edu/datasciencereview/vol3/iss2/5
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