GNPI: Graph normalization to integrate phylogenetic information for metagenomic host phenotype prediction

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成果类型：

期刊论文

作者：

Li, Bojing;Zhong, Duo;Qiao, Jimei;Jiang, Xingpeng

通讯作者：

Xingpeng Jiang

作者机构：

[Zhong, Duo; Jiang, Xingpeng; Li, Bojing] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lear, Wuhan, Peoples R China.

[Zhong, Duo; Jiang, Xingpeng; Li, Bojing] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.

[Qiao, Jimei] Shanghai Normal Univ, Math & Sci Coll, Shanghai, Peoples R China.

[Jiang, Xingpeng] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan, Peoples R China.

通讯机构：

[Xingpeng Jiang] H

Hubei Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, China

语种：

英文

关键词：

Article;artificial neural network;bioinformatics;controlled study;convolutional neural network;data accuracy;data processing;deep learning;graph convolutional network;graph normalization based phylogenetic information;machine learning;metagenomics;phenotype;phylogenetic tree;prediction;human;machine learning;metagenome;phenotype;phylogeny;procedures;Humans;Machine Learning;Metagenome;Metagenomics;Phenotype;Phylogeny

期刊：

Methods

ISSN：

1046-2023

年：

2022

卷：

205

页码：

11-17

DOI：

10.1016/j.ymeth.2022.05.007

基金类别：

The research was supported by the National Natural Science Foundation of China (61872157, and 61932008) and the Key Research and Development Program of Hubei Province (2020BAB017).

机构署名：

本校为第一机构

院系归属：

计算机学院

摘要：

Microorganisms play important roles in our lives especially on metabolism and diseases. Determining the probability of human suffering from specific diseases and the severity of the disease based on microbial genes is the crucial research for understanding the relationship between microbes and diseases. Previous could extract the topological information of phylogenetic trees and integrate them to metagenomic datasets, thus enable classifiers to learn more information in limited datasets and thus improve the performance of the models. In this paper, we proposed a GNPI model to better learn the ...

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GNPI: Graph normalization to integrate phylogenetic information for metagenomic host phenotype prediction

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