Evidence map›Paper›PMID 24995123›Full record

ReviewClinical and translational medicine2014

Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases.

Xiaodan Wu, Luonan Chen, Xiangdong Wang

Abstract readReview
In one paragraph

Review in Clinical and translational medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  17. Can glypican-3 be a disease-specific biomarker?Clinical and translational medicine · 2017
    Review
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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

3 authors.

Xiaodan WuDepartment of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China ; Shanghai Respiratory Research Institute, Shanghai, China.
Luonan ChenKey Laboratory of Systems Biology, SIBS-Novo Nordisk PreDiabetes Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Xiangdong WangDepartment of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China ; Shanghai Respiratory Research Institute, Shanghai, China ; Biomedical Research Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers.

Indexed as

DiagnosisDynamic network biomarkersLung cancerNetwork biomarkersPrognosis

Identifiers

PMID24995123
PMCPMC4072888

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.