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

Mengyuan Wang; Huiru Zheng; Haiying Wang; Richard J. Dewhurst; Rainer Roehe

doi:10.1109/BIBM47256.2019.8983182

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	255-260
Number of pages	6
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983182
Publication status	Print publication - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

KEGG pathway
Metabolomics
Mutual information
NMR analysis
Rumen Microbe

Access to Document

10.1109/BIBM47256.2019.8983182

Cite this

Wang, M., Zheng, H., Wang, H., Dewhurst, R. J., & Roehe, R. (2019). A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 255-260). Article 8983182 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983182

Wang, Mengyuan ; Zheng, Huiru ; Wang, Haiying et al. / A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 255-260 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

@inproceedings{aee4bb00f0074051864f325e77545301,

title = "A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites",

abstract = "Metabolites are the final product of biochemical reactions in the rumen micro-ecological system and very sensitive to changes of microbial genes. However, limited by the spectra library and the computational techniques of structure identification, the identification of metabolites from non-targeted metabolomics is time-consuming and inefficient. The absence of specific information about metabolites makes the biological interpretation of the quantitative analysis of metabolomics meaningless. Based on the nonlinear association between microbial genes and metabolites, combined with knowledge of metabolic pathways from the KEGG database, this study developed a knowledge driven mutual information-based analytical framework for identifying metabolites associated with integrals derived from NMR analysis results. In this study, one known metabolite and three sets of integrals with unknow metabolites were identified within the novel framework. The results showed that this mutual information-based framework could very efficiently target metabolites that may correspond to integrals from NMR spectra.",

keywords = "KEGG pathway, Metabolomics, Mutual information, NMR analysis, Rumen Microbe",

author = "Mengyuan Wang and Huiru Zheng and Haiying Wang and Dewhurst, \{Richard J.\} and Rainer Roehe",

year = "2019",

month = nov,

doi = "10.1109/BIBM47256.2019.8983182",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "255--260",

editor = "Illhoi Yoo and Jinbo Bi and Hu, \{Xiaohua Tony\}",

booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

note = "2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

}

Wang, M, Zheng, H, Wang, H, Dewhurst, RJ & Roehe, R 2019, A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983182, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 255-260, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983182

A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites. / Wang, Mengyuan; Zheng, Huiru; Wang, Haiying et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 255-260 8983182 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites

AU - Wang, Mengyuan

AU - Zheng, Huiru

AU - Wang, Haiying

AU - Dewhurst, Richard J.

AU - Roehe, Rainer

PY - 2019/11

Y1 - 2019/11

N2 - Metabolites are the final product of biochemical reactions in the rumen micro-ecological system and very sensitive to changes of microbial genes. However, limited by the spectra library and the computational techniques of structure identification, the identification of metabolites from non-targeted metabolomics is time-consuming and inefficient. The absence of specific information about metabolites makes the biological interpretation of the quantitative analysis of metabolomics meaningless. Based on the nonlinear association between microbial genes and metabolites, combined with knowledge of metabolic pathways from the KEGG database, this study developed a knowledge driven mutual information-based analytical framework for identifying metabolites associated with integrals derived from NMR analysis results. In this study, one known metabolite and three sets of integrals with unknow metabolites were identified within the novel framework. The results showed that this mutual information-based framework could very efficiently target metabolites that may correspond to integrals from NMR spectra.

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

KW - KEGG pathway

KW - Metabolomics

KW - Mutual information

KW - NMR analysis

KW - Rumen Microbe

UR - https://www.scopus.com/pages/publications/85084340481

U2 - 10.1109/BIBM47256.2019.8983182

DO - 10.1109/BIBM47256.2019.8983182

M3 - Conference contribution

AN - SCOPUS:85084340481

T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

SP - 255

EP - 260

BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

A2 - Yoo, Illhoi

A2 - Bi, Jinbo

A2 - Hu, Xiaohua Tony

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Y2 - 18 November 2019 through 21 November 2019

ER -

Wang M, Zheng H, Wang H, Dewhurst RJ , Roehe R. A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 255-260. 8983182. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983182

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this