Evidence map›Paper›PMID 34348418›Full record

ArticleJournal of integrative bioinformatics2021

Mei Yen Man, Mohd Saberi Mohamad, Yee Wen Choon, Mohd Arfian Ismail

Open access · diamondAbstract read
In one paragraph

Article in Journal of integrative bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 51% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 4 institutions in 2 countries.

Mei Yen ManSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
Mohd Saberi MohamadDepartment of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain 17666, Abu Dhabi, United Arab Emirates.
Yee Wen ChoonInstitute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu 16100, Kelantan, Malaysia; and Department of Data Science, Universiti Malaysia Kelantan, Kota Bharu 16100, Kelantan, Malaysia.
Mohd Arfian IsmailFaculty of Computing (FKOM), College of Computing and Applied Sciences, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia.
United Arab Emirates University · AEUniversiti Malaysia Kelantan · MYUniversiti Malaysia Pahang Al-Sultan Abdullah · MYUniversity of Technology Malaysia · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microorganisms commonly produce many high-demand industrial products like fuels, food, vitamins, and other chemicals. Microbial strains are the strains of microorganisms, which can be optimized to improve their technological properties through metabolic engineering. Metabolic engineering is the process of overcoming cellular regulation in order to achieve a desired product or to generate a new product that the host cells do not usually need to produce. The prediction of genetic manipulations such as gene knockout is part of metabolic engineering. Gene knockout can be used to optimize the microbial strains, such as to maximize the production rate of chemicals of interest. Metabolic and genetic engineering is important in producing the chemicals of interest as, without them, the product yields of many microorganisms are normally low. As a result, the aim of this paper is to propose a combination of the Bat algorithm and the minimization of metabolic adjustment (BATMOMA) to predict which genes to knock out in order to increase the succinate and lactate production rates in

Indexed as

Escherichia coliMetabolic Networks and PathwaysAlgorithmsComputer SimulationGene Knockout TechniquesMetabolic EngineeringBat algorithmbioinformaticsEscherichia coligene knockoutlactateminimization of metabolic adjustmentsuccinate

Identifiers

PMID34348418
PMCPMC8573224
OpenAlexW3190345743

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.