Reiman Derek, Metwally Ahmed
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4269-4272. doi: 10.1109/EMBC.2017.8037799.
The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.
微生物群已被证明会对宿主中各种疾病的发展产生影响。基于基因组样本的微生物分类丰度概况准确预测其表型，是个性化医疗中的一个重要问题。在本文中，我们研究了使用深度学习框架——卷积神经网络（CNN）进行此类预测的潜力。为了便于CNN学习，我们通过创建系统发育树并设计一种将树嵌入矩阵的方案来探索丰度概况的结构，该矩阵保留了树中节点的空间关系及其定量特征。所提出的CNN框架具有很高的准确性，基于对一个包含三种表型的1967个样本的数据集的评估，准确率达到了99.47%。我们的结果证明了CNN在样本表型分类中的可行性和前景。
