MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks Chieh Lo Radu Marculescu 10.1184/R1/10115489.v1 https://kilthub.cmu.edu/articles/journal_contribution/MetaNN_accurate_classification_of_host_phenotypes_from_metagenomic_data_using_neural_networks/10115489 <h3>Background</h3><h3>Microbiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in healthy and sick individuals for a variety of diseases; this suggests that the microbiome profile can be used as a diagnostic tool in identifying the disease states of an individual. However, the high-dimensional nature of metagenomic data poses a significant challenge to existing machine learning models. Consequently, to enable personalized treatments, an efficient framework that can accurately and robustly differentiate between healthy and sick microbiome profiles is needed.</h3><h3><br></h3><h3>Results</h3><h3>In this paper, we propose MetaNN (i.e., classification of host phenotypes from Metagenomic data using Neural Networks), a neural network framework which utilizes a new data augmentation technique to mitigate the effects of data over-fitting.</h3><h3><br></h3><h3>Conclusions</h3><h3>We show that MetaNN outperforms existing state-of-the-art models in terms of classification accuracy for both synthetic and real metagenomic data. These results pave the way towards developing personalized treatments for microbiome related diseases.</h3> 2019-10-31 21:19:35 Metagenomics Neural networks Host phenotypes Machine learning