A Deep Learning Framework For Human Disease Prediction Using Microbiome Data

More and more research points to the microbiome's potential as a disease predictor, and the human microbiota is already playing an important role in human health. Microbiome data is notoriously high-dimensional (on the order of hundreds of thousands of dimensions), and prediction methods based on machine learning have a tough time with small sample numbers. Because of this disparity, the data is extremely scarce, which hinders the ability to train a more accurate prediction model. Existing approaches sometimes overlook taxonomic connections across microbial species or fail to account for plenty profiles from both known &nknown microbial-organisms, resulting in a substantial loss of knowledge. However, because to its exceptional feature-learning capability, deep learning has demonstrated unparalleled benefits in categorization tasks. On the other hand, it runs into trouble with metagenome-based illness prediction due to the fact that black-box models don't provide biological explanations and high-dimensional, low-sample-size metagenomic datasets might cause overfitting. Our solution to these issues is MetaDR, an all-encompassing framework for disease prediction in humans that makes use of deep learning and a wide range of data sources. The experimental findings show that MetaDR successfully finds the informative features using biological insights, and it achieves competitive prediction performance while reducing running time.