Evidence map›Paper›PMID 35247965›Full record

ArticleBMC bioinformatics2022

DrGA: cancer driver gene analysis in a simpler manner.

Quang-Huy Nguyen, Tin Nguyen, Duc-Hau Le

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2022. 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. 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.

Quang-Huy NguyenSchool of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.
Tin NguyenDepartment of Computer Science and Engineering, University of Nevada, Reno, NV, USA.
Duc-Hau LeSchool of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam. hauldhut@gmail.com.ORCID http://orcid.org/0000-0002-4951-5916

Funding

Outreach/Diversity/Pipeline ProjectP20GM103440 · NIGMS · UNIVERSITY OF NEVADA RENO · PI Julia Petereit · 2012 to 2026
$58.0M
NIGMS NIH HHS GM103440NIGMS NIH HHS P20 GM103440
6 · The paper itself

Abstract

backgroundTo date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient.

resultsThis study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism.

conclusionsDrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA .

Indexed as

Breast NeoplasmsOncogenesBiomarkers, TumorFemaleHumansMutationBiomarkers, TumorClinical featureDriver geneGenetic biomakerHuman breast cancerMouse metabolic syndromeOmics data

Identifiers

PMID35247965
PMCPMC8897886

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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.