A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites

Mengyuan Wang, Huiru Zheng, Haiying / HY Wang, Richard J. Dewhurst, Rainer Roehe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Metabolites as the final product of biochemical reactions in the rumen micro-ecological system are very sensitive to changes in microbial genes. However, limited by the metabolite database and software platform analysis techniques, the identification of metabolites is often time-consuming. The absence of specific information of metabolites derived the biological interpretation of the quantitative analysis of metabolomics become meaningless. Based on the nonlinear association between microbial genes and metabolites, combined with the knowledge of metabolic pathways in the KEGG database, this study developed a knowledge driven mutual information-based analytical framework for identifying unknown integrals in NMR analysis results. In this study, one known metabolite and three sets of unknown integrals derived from quantitatively analysis were identified. The results showed that this mutual information-based framework could very efficiently targeted metabolites that may correspond to unknown integrals. 

Original languageEnglish
Title of host publication2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)
Place of PublicationSan Diego, CA, USA
PublisherIEEE
Pages255-260
Number of pages5
ISBN (Electronic)978-1-7281-1867-3
ISBN (Print)978-1-7281-1868-0
DOIs
Publication statusPublished - 6 Feb 2020
Event2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - San Diego, CA, USA
Duration: 18 Nov 201921 Nov 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Period18/11/1921/11/19

Keywords

  • Metabolomics
  • NMR analysis
  • KEGG pathway
  • Mutual information
  • Rumen Microbe

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