Evidence map›Paper›PMID 31286073›Full record

ArticleHeliyon2019

iMet: A graphical user interface software tool to merge metabolic networks.

Reza Mohammadi, Javad Zahiri, Mohammad Javad Niroomand

Abstract read
In one paragraph

Article in Heliyon, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. NAR genomics and bioinformatics · 2024
    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.

Reza MohammadiMalek Ashtar University of Technology, Iran.
Javad ZahiriBioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
Mohammad Javad NiroomandLearning Intelligent Systems Lab, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, studying microorganisms has become faster and deeper than the last decades, thanks to the modeling of genome-scale metabolic networks. Completed genome sequencing projects of microorganisms and annotating these sequences have provided a worthwhile platform for reconstructing and modeling genome-scale metabolic networks. The genome-scale metabolic network reconstruction is a laborious and time-consuming task which needs an extensive study and search in different types of databases. Furthermore, it also requires an iterative process of creating and curating the obtained network, particularly with experimental methods. Hence, different types of reconstructions and models of a targeted microorganism can be found with different qualities, as the goal and need of researchers differ. Due to these circumstances, scientists have to continue with only one of the reconstructed metabolic networks of each microorganism and ignore the rest in their

Indexed as

BioinformaticsBiotechnologyMicrobiologySystems biology

Identifiers

PMID31286073
PMCPMC6587100

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.