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.
Urinary Glycoprotein Biomarker Discovery for Bladder Cancer Detection Using LC/MS-MS and Label-Free Quantification
