Evidence map›Paper›PMID 32020158›Full record

ReviewBriefings in bioinformatics2021

Human body-fluid proteome: quantitative profiling and computational prediction.

Lan Huang, Dan Shao, Yan Wang, Xueteng Cui, Yufei Li, Qian Chen, Juan Cui

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lan HuangCollege of Computer Science and Technology in the Jilin University.
Dan ShaoCollege of Computer Science and Technology in the Jilin University.
Yan WangCollege of Computer Science and Technology in the Jilin University.
Xueteng CuiCollege of Computer Science and Technology in the Changchun University.
Yufei LiCollege of Computer Science and Technology in the Changchun University.
Qian ChenCollege of Computer Science and Technology in the Jilin University.
Juan CuiDepartment of Computer Science and Engineering in the University of Nebraska-Lincoln.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BiomarkersBody FluidsHumansProteomeProteomicsBiomarkersProteomebiomarker discoverybody-fluid proteomeclinical applicationprotein prediction

Identifiers

PMID32020158
PMCPMC7820883

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.