Evidence map›Paper›PMID 27781158›Full record

ArticlePeerJ2016

Formal modeling and analysis of ER-

Samra Khalid, Rumeza Hanif, Samar H K Tareen, Amnah Siddiqa, Zurah Bibi, Jamil Ahmad

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2016. 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
0.9field-weighted citation impact, top 23% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Samra KhalidAtta-ur-Rahman School of Applied Biosciences (ASAB)/Healthcare Biotechnology, National University of Science and Technology , Islamabad , Pakistan.
Rumeza HanifAtta-ur-Rahman School of Applied Biosciences (ASAB)/Healthcare Biotechnology, National University of Science and Technology , Islamabad , Pakistan.
Samar H K TareenMaastricht Centre for Systems Biology (MaCSBio), Maastricht University , Maastricht , Netherlands.
Amnah SiddiqaResearch Center for Modeling & Simulation (RCMS), National University of Science and Technology , Islamabad , Pakistan.
Zurah BibiResearch Center for Modeling & Simulation (RCMS), National University of Science and Technology , Islamabad , Pakistan.
Jamil AhmadResearch Center for Modeling & Simulation (RCMS), National University of Science and Technology , Islamabad , Pakistan.
Maastricht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-

methodsA discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities.

results

conclusionThe

Indexed as

Biological Regulatory NetworkBreast cancerContinuous Hybrid Petri NetDiscrete and continuous modelingEstrogen receptor-alphaGENOTECHRené Thomas formalismSMBioNet

Identifiers

PMID27781158
PMCPMC5075711
OpenAlexW2534446951

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.