Robust biomarker discovery for microbiome-wide association studies

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Zhu, Qiang;Li, Bojing;He, Tingting（何婷婷）;Li, Guangrong;Jiang, Xingpeng*（蒋兴鹏）

通讯作者：

Jiang, Xingpeng

作者机构：

[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.

[Jiang, Xingpeng; He, Tingting; Li, Bojing] Cent China Normal Univ, Sch Comp, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.

[Jiang, Xingpeng; He, Tingting; Zhu, Qiang; Li, Bojing] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Hubei, Peoples R China.

[Li, Guangrong] Hunan Univ, Sch Business, Changsha, Hunan, Peoples R China.

通讯机构：

[Jiang, Xingpeng] C

Cent China Normal Univ, Sch Comp, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.

语种：

英文

关键词：

biological marker;biological marker;Article;convolutional neural network;deep forest;deep learning;deep neural network;gene sequence;high throughput sequencing;information processing;k nearest neighbor;logistic regression analysis;mathematical computing;metagenomics;microbiome;priority journal;support vector machine;genetics;genome-wide association study;human;medical research;microflora;procedures;Biomarkers;Biomedical Research;Genome-Wide Association Study;Humans;Microbiota;Neural Networks, Computer

期刊：

Methods

ISSN：

1046-2023

年：

2020

卷：

173

页码：

44-51

DOI：

10.1016/j.ymeth.2019.06.012

基金类别：

This research is supported by the National Key Research and Development Program of China ( 2017YFC0909502 ) and the National Natural Science Foundation of China (No. 61532008 and 61872157 ).

机构署名：

本校为第一且通讯机构

院系归属：

计算机学院

信息管理学院

摘要：

According to the advances of high-throughput sequencing technology, massive microbiome data accumulated from environmental investigations to human studies. The microbiome-wide association studies are to study the relationship between the microbiome and human health or environment. Recently, Deep Neural Networks (DNNs) are encouraging due to their layer-wise learning ability for representation learning. However, DNNs are considered as black boxes and they require a large amount of training data which makes them impractical to conduct microbiome-...

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Robust biomarker discovery for microbiome-wide association studies

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