Evidence map›Paper›PMID 22287794›Full record

ReviewBriefings in bioinformatics2012

Identification of aberrant pathways and network activities from high-throughput data.

Jinlian Wang, Yuji Zhang, Catalin Marian, Habtom W Ressom

Open access · bronzeAbstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
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

15 citing papers in PubMed, 31 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Multi-omic Pathway and Network Analysis to Identify Biomarkers for Hepatocellular Carcinoma.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019
    Article
  5. Review
  6. Article
  7. Article
  8. Data integration in biological research: an overview.Journal of biological research (Thessalonike, Greece) · 2015
    Review
  9. Applications of systems approaches in the study of rheumatic diseases.The Korean journal of internal medicine · 2015
    Review
  10. Review
  11. Review
  12. Article
  13. Multiscale models of breast cancer progression.Annals of biomedical engineering · 2012
    Review
  14. Article
  15. Genomics and bioinformatics of Parkinson's disease.Cold Spring Harbor perspectives in medicine · 2012
    Review
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

4 authors at 1 institution in 1 country.

Jinlian WangLombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.
Yuji Zhang
Catalin Marian
Habtom W Ressom
Georgetown University Medical Center · US

Funding

Quantitative analysis of LC-MS data for peptide and glycan biomarker discoveryR01GM086746 · NIGMS · GEORGETOWN UNIVERSITY · PI RESSOM, HABTOM W · 2010 to 2014
$1.3M
NIGMS NIH HHS R01 GM086746NIGMS NIH HHS R01GM086746
6 · The paper itself

Abstract

Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.

Indexed as

Gene Regulatory NetworksHigh-Throughput Screening AssaysDatabases, FactualSystems Biology

Identifiers

PMID22287794
PMCPMC3404398
OpenAlexW2154675099

What OpenQuestion holds

Textmetadata
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.