Deep learning for improved microbial community profiling through 16S rDNA Data

Microbial community characterization is important, especially for the identification of microbial species and their relationships within various environments in support of clinical, agricultural, and ecological applications. Although morphological and culture-independent molecular techniques using 16S rDNA gene sequencing have been extensively employed, they are less efficient, particularly for determining the real presence of minor communities in a sample. In this study, deep learning models, CNN and RNN, were applied to improve the classification and characterization of microorganisms describing the 16S rDNA sequence data. From the current experiment, it is postulated that the incorporation of both CNNs for precise pattern recognition and RNNs for the latent dependencies on detection enhance accuracy, especially for species with a lower probability of detection. Public datasets were used to evaluate the models. The performance of the proposed models was also assessed in relation to the basic machine learning methods. According to the study, classification accuracy could be enhanced by using deep learning-based solutions to overcome existing limitations in the description of microbial diversity. These advancements have enormous potential for many disciplines, ranging from disease diagnosis to soil and water examinations, based on improving the ability to analyze ability to analyze the microbial community.