Literature DB >> 26519180

Data Fusion in Metabolomics and Proteomics for Biomarker Discovery.

Lionel Blanchet1,2, Agnieszka Smolinska3.   

Abstract

Proteomics and metabolomics provide key insights into status and dynamics of biological systems. These molecular studies reveal the complex mechanisms involved in disease or aging processes. Invaluable information can be obtained using various analytical techniques such as nuclear magnetic resonance, liquid chromatography, or gas chromatography coupled to mass spectrometry. Each method has inherent advantages and drawbacks, but they are complementary in terms of biological information.The fusion of different measurements is a complex topic. We describe here a framework allowing combining multiple data sets, provided by different analytical platforms. For each platform, the relevant information is extracted in the first step. The obtained latent variables are then fused and further analyzed. The influence of the original variables is then calculated back and interpreted.

Keywords:  Chemometrics; Discriminant analysis; Latent variable; PLS-DA; Variable selection; eCVA

Mesh:

Substances:

Year:  2016        PMID: 26519180     DOI: 10.1007/978-1-4939-3106-4_14

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  7 in total

1.  Clinical Research Informatics for Big Data and Precision Medicine.

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Journal:  Yearb Med Inform       Date:  2016-11-10

2.  Data Fusion Approach to Simultaneously Evaluate the Degradation Process Caused by Ozone and Humidity on Modern Paint Materials.

Authors:  Laura Pagnin; Rosalba Calvini; Katja Sterflinger; Francesca Caterina Izzo
Journal:  Polymers (Basel)       Date:  2022-04-27       Impact factor: 4.967

Review 3.  Mathematical and Computational Modeling in Complex Biological Systems.

Authors:  Zhiwei Ji; Ke Yan; Wenyang Li; Haigen Hu; Xiaoliang Zhu
Journal:  Biomed Res Int       Date:  2017-03-13       Impact factor: 3.411

4.  Metabolomic Biomarker Identification in Presence of Outliers and Missing Values.

Authors:  Nishith Kumar; Md Aminul Hoque; Md Shahjaman; S M Shahinul Islam; Md Nurul Haque Mollah
Journal:  Biomed Res Int       Date:  2017-02-14       Impact factor: 3.411

5.  Kernel weighted least square approach for imputing missing values of metabolomics data.

Authors:  Nishith Kumar; Md Aminul Hoque; Masahiro Sugimoto
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.379

6.  Serum and Plasma Metabolomic Biomarkers for Lung Cancer.

Authors:  Nishith Kumar; Md Shahjaman; Md Nurul Haque Mollah; S M Shahinul Islam; Md Aminul Hoque
Journal:  Bioinformation       Date:  2017-06-30

7.  Robust volcano plot: identification of differential metabolites in the presence of outliers.

Authors:  Nishith Kumar; Md Aminul Hoque; Masahiro Sugimoto
Journal:  BMC Bioinformatics       Date:  2018-04-11       Impact factor: 3.169

  7 in total

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