Human body-fluid proteome: quantitative profiling and computational prediction.
biomarker discovery
body-fluid proteome
clinical application
protein prediction
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
18 01 2021
18 01 2021
Historique:
received:
05
07
2019
revised:
22
08
2019
accepted:
18
10
2019
pubmed:
6
2
2020
medline:
16
11
2021
entrez:
6
2
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32020158
pii: 5722395
doi: 10.1093/bib/bbz160
pmc: PMC7820883
doi:
Substances chimiques
Biomarkers
0
Proteome
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
315-333Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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