Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection | Bentham Science
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Review Article

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

Author(s): Jianbo Fu, Yongchao Luo, Minjie Mou, Hongning Zhang, Jing Tang, Yunxia Wang and Feng Zhu*

Volume 21, Issue 1, 2020

Page: [34 - 54] Pages: 21

DOI: 10.2174/1389450120666190821160207

Price: $65

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Abstract

Background: Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

Objective: The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

Methods: Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

Results: In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

Conclusion: In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Keywords: Label free quantification, diabetes proteomics, computation, target discovery, antidiabetic drug, mass spectrometry.

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