当前位置: X-MOL 学术IEEE J. Biomed. Health Inform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype from Metagenomic Data.
IEEE Journal of Biomedical and Health Informatics ( IF 6.7 ) Pub Date : 2020-05-11 , DOI: 10.1109/jbhi.2020.2993761
Derek Reiman , Ahmed A Metwally , Jun Sun , Yang Dai

Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introduce PopPhy-CNN, a novel convolutional neural network (CNN) learning framework that effectively exploits phylogenetic structure in microbial taxa for host phenotype prediction. Our approach takes an input format of a 2D matrix representing the phylogenetic tree populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. We show the competitiveness of our model compared to other available methods using nine metagenomic datasets of moderate size for binary classification. With synthetic and biological datasets, we show the superior and robust performance of our model for multi-class classification. Furthermore, we design a novel scheme for feature extraction from the learned CNN models and demonstrate improved performance when the extracted features. PopPhy-CNN is a practical deep learning framework for the prediction of host phenotype with the ability of facilitating the retrieval of predictive microbial taxa.

中文翻译:

PopPhy-CNN:用于卷积神经网络的系统发育树嵌入式体系结构,可从超基因组数据预测宿主表型。

从宏基因组学样本中准确预测宿主表型和鉴定相关的微生物标记,对于理解与疾病发生和发展有关的潜在宿主-微生物组相互作用非常重要。我们介绍PopPhy-CNN,这是一种新颖的卷积神经网络(CNN)学习框架,可有效利用微生物分类群中的系统发育结构进行宿主表型预测。我们的方法采用2D矩阵的输入格式,该矩阵表示系统发育树,其中包含宏基因组学样本中相对丰富的微生物分类单元。这种转换使CNN能够探索树上分类注释的空间关系及其在宏基因组数据中的定量特征。与使用九个中等大小的宏基因组数据集进行二进制分类的其他可用方法相比,我们显示了该模型的竞争力。借助合成和生物学数据集,我们展示了我们的模型用于多类分类的优越且强大的性能。此外,我们设计了一种从学习的CNN模型中提取特征的新颖方案,并在提取特征时展示了改进的性能。PopPhy-CNN是用于预测宿主表型的实用深度学习框架,具有促进检索预测性微生物分类群的能力。我们设计了一种从学习的CNN模型中提取特征的新颖方案,并在提取特征时展示了改进的性能。PopPhy-CNN是用于预测宿主表型的实用深度学习框架,具有促进检索预测性微生物分类群的能力。我们设计了一种从学习的CNN模型中提取特征的新颖方案,并在提取特征时展示了改进的性能。PopPhy-CNN是用于预测宿主表型的实用深度学习框架,具有促进检索预测性微生物分类群的能力。
更新日期:2020-05-11
down
wechat
bug