Evidence map›Paper›PMID 18665624›Full record

ArticleJournal of proteome research2008

The knowledge-integrated network biomarkers discovery for major adverse cardiac events.

Guangxu Jin, Xiaobo Zhou, Honghui Wang, Hong Zhao, Kemi Cui, Xiang-Sun Zhang, Luonan Chen, Stanley L Hazen, King Li, Stephen T C Wong

Abstract read
In one paragraph

Article in Journal of proteome research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–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

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Data analysis methods for defining biomarkers from omics data.Analytical and bioanalytical chemistry · 2022
    Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Network biology in medicine and beyond.Circulation. Cardiovascular genetics · 2014
    Article
  17. Review
  18. Article
  19. Review
  20. Article
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

10 authors.

Guangxu JinCenter for Biotechnology and Informatics, The Methodist Hospital Research Institute & Cornell University, Houston, Texas 77030, USA.
Xiaobo Zhou
Honghui Wang
Hong Zhao
Kemi Cui
Xiang-Sun Zhang
Luonan Chen
Stanley L Hazen
King Li
Stephen T C Wong

Funding

Neuronal Spines Tracking and Analysis for Time-Lapse, 3D Optical MicroscopyR01LM009161 · NLM · METHODIST HOSPITAL RESEARCH INSTITUTE · PI WONG, STEPHEN TC · 2007 to 2010
$1.3M
High-content Image Analysis and Modeling for Neuron Assay Based ScreeningR01AG028928 · NIA · METHODIST HOSPITAL RESEARCH INSTITUTE · PI WONG, STEPHEN TC · 2007 to 2010
$1.3M
Cell tracking and analysis for time-lapse microscopyR01LM008696 · NLM · METHODIST HOSPITAL RESEARCH INSTITUTE · PI WONG, STEPHEN TC · 2005 to 2007
$1.0M
NIA NIH HHS R01 AG028928NIA NIH HHS R01AG028928NLM NIH HHS R01 LM008696NLM NIH HHS R01 LM009161NLM NIH HHS R01LM009161NLM NIH HHS R01LM08696
6 · The paper itself

Abstract

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.

Indexed as

BiomarkersCardiovascular DiseasesHumansMass SpectrometrySystems BiologyBiomarkers

Identifiers

PMID18665624
PMCPMC2854538

What OpenQuestion holds

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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.