Deep Learning Classification of Caries Based on DNA Sequences Transformed by Chaos Game Representation

Frequency Chaos Game Representation (FCGR) is a method where DNA sequences are converted into a 2D image representing k-mer nucleotide frequencies with additional geometric fractal patterns. We used FCGR to analyze nextgeneration sequences (NGS) of 16S rRNA gene amplicons derived from oral samples with or without caries in combination with machine deep learning to differentiate disease from health. The NGS data captured 16S rRNA genes of all species in the oral cavity for studying oral microbiome, which has been shown to associate with the progression of caries. Our hypothesis is that FCGR patterns of caries and caries-free samples are unique and can be differentiated by deep learning using convolutional neural network model (CNN) - a model specialized in classifying images. Samples labeled as caries and caries-free were split into 80-10-10 for training, testing, and validation purposes in CNN training. An accuracy of 87.5% was achieved in this pilot study. Our study showed that FCGR transforms DNA sequences into fractal image patterns and can be applied to oral microbiome NGS data for classifying clinical oral samples as caries or caries-free. This approach allows for fast and early diagnosis and prevention of major cavities.