Evidence map›Paper›PMID 24808933›Full record

ArticleCognitive neurodynamics2014

Evaluating influence of microRNA in reconstructing gene regulatory networks.

Ahsan Raja Chowdhury, Madhu Chetty, Nguyen Xuan Vinh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cognitive neurodynamics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Epigenetics in the pathogenesis of diabetic nephropathy.Acta biochimica et biophysica Sinica · 2022
    Review
  2. Review
  3. Article
  4. Review
  5. 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 at 1 institution in 1 country.

Ahsan Raja ChowdhuryGippsland School of Information Technology, Monash University, Victoria, Australia ; National ICT Australia (NICTA), VRL, Melbourne, Australia.
Madhu ChettyGippsland School of Information Technology, Monash University, Victoria, Australia ; National ICT Australia (NICTA), VRL, Melbourne, Australia.
Nguyen Xuan VinhGippsland School of Information Technology, Monash University, Victoria, Australia.
Monash University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.

Indexed as

Gene regulatory networkMicroarraymicroRNA

Identifiers

PMID24808933
PMCPMC4012069
OpenAlexW1988643022

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.