Evidence map›Paper›PMID 25750554›Full record

ReviewThe Korean journal of internal medicine2015

Applications of systems approaches in the study of rheumatic diseases.

Ki-Jo Kim, Saseong Lee, Wan-Uk Kim

Abstract readReview
In one paragraph

Review in The Korean journal of internal medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

3 authors.

Ki-Jo KimDivision of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea.
Saseong LeePOSTECH-CATHOLIC BioMedical Engineering Institute, The Catholic University of Korea, Seoul, Korea.
Wan-Uk KimPOSTECH-CATHOLIC BioMedical Engineering Institute, The Catholic University of Korea, Seoul, Korea. ; Division of Rheumatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.

Indexed as

Rheumatic DiseasesSystems BiologyAnimalsAntirheumatic AgentsBiomedical ResearchCytokinesGenetic MarkersGenetic Predisposition to DiseaseHumansInflammation MediatorsMolecular Targeted TherapyPhenotypePrognosisRheumatologyRisk FactorsSignal TransductionAntirheumatic AgentsCytokinesGenetic MarkersInflammation MediatorsNetwork analysisRheumatic diseasesSystems biology

Identifiers

PMID25750554
PMCPMC4351319

What OpenQuestion holds

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LicenceCC BY-NC
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.