Evidence map›Paper›PMID 37293242›Full record

ArticleComputational and structural biotechnology journal2023

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.

Xiaolu Xu, Zitong Qi, Dawei Zhang, Meiwei Zhang, Yonggong Ren, Zhaohong Geng

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2023. 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
0.5field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 4 institutions in 2 countries.

Xiaolu XuSchool of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China.
Zitong QiDepartment of Statistics, University of Washington, Seattle, WA 98195, USA.
Dawei ZhangSchool of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China.
Meiwei ZhangCenter for Reproductive and Genetic Medicine, Dalian Women and Children's Medical Group, Dalian 116037, China.
Yonggong RenSchool of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China.
Zhaohong GengDepartment of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China.
Liaoning Normal University · CNDalian Maternal and Child Health Hospital · CNDalian Medical University · CNUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined

Indexed as

Cancer researchDriver geneDriver pathwayMutSigCVStatistical methods

Identifiers

PMID37293242
PMCPMC10244682
OpenAlexW4378473117

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.