Using deep learning for the taxonomic classification of microbial sequences

This study applies an effective deep learning framework for the classification of microbial sequences. The proposed architecture combines a sequence embedding layer with other layers of a bidirectional Long Short-Term Memory, Seft-attention, and Dropout mechanisms for feature learning. Experimental results demonstrate the strength of the proposed method on datasets of real metagenomes.