A machine learning framework of functional biomarker discovery for different microbial communities based on metagenomic data
As more than 90% of microbial community could not be isolated and cultivated,the metagenomic methods have been commonly used to analyze the microbial community as a whole.With the fast acumination of metagenomic samples,it is now intriguing to find simple biomarkers,especially functional biomarkers,which could distinguish different metagenomic samples.Next-generation sequencing techniques have enabled the detection of very accurate gene-presence (abundance) values in metagenomic studies.And the presence/absence or different abundance values for a set of genes could be used as appropriate biomarker for identification of the corresponding microbial community’s phenotype.However,it is not yet clear how to select such a set of genes (features),and how accurate would it be for such a set of selected genes on prediction of microbial community’s phenotype.In this study,we have evaluated different machine learning methods,including feature selection methods and classification methods,for selection of biomarkers that could distinguish different samples.Then we proposed a machine learning framework,which could discover biomarkers for different microbial communities from the mining of metagenomic data.Given a set of features (genes) and their presence values in multiple samples,we first selected discriminative features as candidate by feature selection,and then selected the feature sets with low error rate and classification accuracies as biomarkers by classification method.We have selected whole genome sequencing data from simulation,public domain and in-house metagenomic data generation facilities.We tested the framework on prediction and evaluation of the biomarkers.Results have shown that the framework could select functional biomarkers with very high accuracy.Therefore,this framework would be a suitable tool to discover functional biomarkers to distinguish different microbial communities.
metagenomic biomarker machine learning ReliefF mRMR
Wei Fang Xingzhi Chang Xiaoquan Su Jian Xu Deli Zhang Kang Ning
Investigation Group of Molecular Virology, Immunology, Oncology & Systems Biology, Center for Bioinf Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shan Investigation Group of Molecular Virology, Immunology, Oncology & Systems Biology, Center for Bioinf
国际会议
6th International Conference on Systems Biology (第六届国际系统生物学会议)(ISB2012)
西安
英文
106-112
2012-08-19（万方平台首次上网日期，不代表论文的发表时间）