Deep Ensemble Models for 16S Ribosomal Gene Classification

摘要

In bioinformatics analysis, the correct identification of an unknown sequence by subsequent matching with a known sequence is a crucial and critical initial step. One of the constantly evolving open and challenging areas of research is understanding the adaptation of microbiome communities derived from different environment as well as human gut. The critical component of such studies is to analyze 16s rRNA gene sequence and classify it to a corresponding taxonomy. Thus far recent literature discusses such sequence classification tasks being solved using many algorithms such as early methods of k-mer frequency matching, and assembly-based clustering or advanced methods of machine learning algorithms- for instance, random forests, naive Bayesian techniques, and recently deep learning architectures. Our previous work focused on a comprehensive study of 16s rRNA gene classification by implementing simplistic singular neural models of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). The outcome of this study demonstrated very promising classification results for family, genus and species taxonomic levels, prompting an immediate investigation into deep ensemble models for problem at hand. In this study, we attempt to classify 16s rRNA gene using deep ensemble models along with a hybrid model that emulates an ensemble in its early convolutional layers followed by a recurrent layer.