Evidence map›Paper›PMID 24381858›Full record

ArticleCell journal2014

Network analysis of transcription factors for nuclear reprogramming into induced pluripotent stem cell using bioinformatics.

Chiranjib Chakraborty, Sanjiban S Roy, Minna J Hsu, Govindasamy Agoramoorthy

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Cell journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. miRNA-regulated cancer stem cells: understanding the property and the role of miRNA in carcinogenesis.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016
    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.

Chiranjib Chakraborty1.Department of Bio-Informatics, School of Computer and Information Sciences, Galgotias University, Greater Noida, India.
Sanjiban S Roy2.School of Computing Science and Engineering, VIT University, Vellore, India.
Minna J Hsu3.Department of Biological Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Govindasamy Agoramoorthy4.College of Pharmacy and Health Care, Tajen University, Yanpu, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveResearch related to induce pluripotent stem (iPS) cell generation has increased rapidly in recent years. Six transcription factors, namely OCT4, SOX2, C-MYC, KLF4, NANOG, and LIN28 have been widely used for iPS cell generation. As there is a lack of data on intra- and inter-networking among these six different transcription factors, the objective of this study is to analyze the intra- and inter-networks between them using bioinformatics. MATERIALS AND

methodsIn this computational biology study, we used AminoNet, MATLAB to examine networking between the six different transcription factors. The directed network was constructed using MATLAB programming and the distance between nodes was estimated using a phylogram. The protein-protein interactions between the nuclear reprogramming factors was performed using the software STRING.

resultsThe relationship between C-MYC and NANOG was depicted using a phylogenetic tree and the sequence analysis showed OCT4, C-MYC, NANOG, and SOX2 together share a common evolutionary origin.

conclusionThis study has shown an innovative rapid method for the analysis of intra and inter-networking among nuclear reprogramming factors. Data presented may aid researchers to understand the complex regulatory networks involving iPS cell generation.

Indexed as

Computational BiologyGene NetworkNuclear ReprogrammingTranscription Factors

Identifiers

PMID24381858
PMCPMC3866537

What OpenQuestion holds

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Registered trials

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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.