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

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 / HY Wang, Richard J. Dewhurst, Rainer Roehe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)
Place of Publication	San Diego, CA, USA
Publisher	IEEE
Pages	255-260
Number of pages	5
ISBN (Electronic)	978-1-7281-1867-3
ISBN (Print)	978-1-7281-1868-0
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983182
Publication status	Published (in print/issue) - 6 Feb 2020
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - San Diego, CA, USA Duration: 18 Nov 2019 → 21 Nov 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Period	18/11/19 → 21/11/19

Keywords

Metabolomics
NMR analysis
KEGG pathway
Mutual information
Rumen Microbe

UN SDGs

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

Access to Document

10.1109/BIBM47256.2019.8983182

Cite this

@inproceedings{e56156b333a144d090e547e012dfb945,

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

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. ",

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

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

year = "2020",

month = feb,

day = "6",

doi = "10.1109/BIBM47256.2019.8983182",

language = "English",

isbn = "978-1-7281-1868-0",

pages = "255--260",

booktitle = "2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)",

publisher = "IEEE",

address = "United States",

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

}

Wang, M, Zheng, H , Wang, HHY, Dewhurst, RJ & Roehe, R 2020, A knowledge driven mutual information-based analytical framework for the identification of rumen metabolites. in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019). IEEE, San Diego, CA, USA, pp. 255-260, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 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 / HY et al.
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019). San Diego, CA, USA: IEEE, 2020. p. 255-260.

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 / HY

AU - Dewhurst, Richard J.

AU - Roehe, Rainer

PY - 2020/2/6

Y1 - 2020/2/6

N2 - 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.

AB - 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.

KW - Metabolomics

KW - NMR analysis

KW - KEGG pathway

KW - Mutual information

KW - Rumen Microbe

U2 - 10.1109/BIBM47256.2019.8983182

DO - 10.1109/BIBM47256.2019.8983182

M3 - Conference contribution

SN - 978-1-7281-1868-0

SP - 255

EP - 260

BT - 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)

PB - IEEE

CY - San Diego, CA, USA

T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Y2 - 18 November 2019 through 21 November 2019

ER -

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

Abstract

Conference

Keywords

UN SDGs

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Cite this