Metagenome-Based Disease Classification with Deep Learning and Visualizations Based on Self-organizing Maps

Abstract

Machine learning algorithms have recently revealed impressive results across a variety of biology and medicine domains. The applications of machine learning in bioinformatics include predicting of biological processes (for example, prediction tasks on gene function), prevention of diseases and personalized treatment. In the last decade, deep learning has gained an impressive success on a variety of problems such as speech recognition, image classification, and natural language processing. Among various methodological variants of deep learning networks, the Convolutional Neural Networks (CNN) have been extensively studied, especially in the field of image processing. Moreover, Data visualization is considered as an indispensable technique for the exploratory data analysis and becomes a key for discoveries. In this paper, a novel approach based on visualization capabilities of Self-Organizing Maps and deep learning is proposed to not only visualize metagenomic data but also leverage advances in deep learning to improve the disease prediction. Several solutions are also introduced to reduce negative affects of overlapped points to enhance the performance. The proposed approach is evaluated on six metagenomic datasets using species abundance. The results reveal that the proposed visualization not only shows improvements in the performance but also allows to visualize biomedical signatures.