TY - JOUR
T1 - Human body-fluid proteome
T2 - Quantitative profiling and computational prediction
AU - Huang, Lan
AU - Shao, Dan
AU - Wang, Yan
AU - Cui, Xueteng
AU - Li, Yufei
AU - Chen, Qian
AU - Cui, Juan
N1 - Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.
AB - Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.
KW - biomarker discovery
KW - body-fluid proteome
KW - clinical application
KW - protein prediction
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U2 - 10.1093/bib/bbz160
DO - 10.1093/bib/bbz160
M3 - Review article
C2 - 32020158
AN - SCOPUS:85100280770
SN - 1467-5463
VL - 22
SP - 315
EP - 333
JO - Briefings in bioinformatics
JF - Briefings in bioinformatics
IS - 1
ER -