Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks

TL;DR

This paper introduces a novel method to apply convolutional neural networks to metagenomic data by transforming it into 2D images, enabling disease prediction with promising results across multiple datasets.

Contribution

The paper demonstrates how to meaningfully map metagenomic data into 2D images for CNN application, addressing the challenge of non-image structured data in bioinformatics.

Findings

Effective disease prediction across six datasets

Successful mapping of metagenomic data to 2D images

Potential of CNNs for bioinformatics prediction tasks

Abstract

Deep learning (DL) techniques have had unprecedented success when applied to images, waveforms, and texts to cite a few. In general, when the sample size (N) is much greater than the number of features (d), DL outperforms previous machine learning (ML) techniques, often through the use of convolution neural networks (CNNs). However, in many bioinformatics ML tasks, we encounter the opposite situation where d is greater than N. In these situations, applying DL techniques (such as feed-forward networks) would lead to severe overfitting. Thus, sparse ML techniques (such as LASSO e.g.) usually yield the best results on these tasks. In this paper, we show how to apply CNNs on data which do not have originally an image structure (in particular on metagenomic data). Our first contribution is to show how to map metagenomic data in a meaningful way to 1D or 2D images. Based on this representation, we then apply a CNN, with the aim of predicting various diseases. The proposed approach is applied on six different datasets including in total over 1000 samples from various diseases. This approach could be a promising one for prediction tasks in the bioinformatics field.