Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks
This addresses the problem of overfitting in bioinformatics for researchers, though it is incremental as it adapts existing CNN methods to a new data type.
The paper tackled the challenge of applying deep learning to high-dimensional, low-sample-size metagenomic data by mapping it to 1D or 2D images and using CNNs for disease prediction, achieving promising results across six datasets with over 1000 samples.
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.