Evidence map›Paper›PMID 38091511›Full record

ArticleGigaScience2022

DriverMP enables improved identification of cancer driver genes.

Yangyang Liu, Jiyun Han, Tongxin Kong, Nannan Xiao, Qinglin Mei, Juntao Liu

Open access · goldAbstract read
In one paragraph

Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 50% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
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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 2 institutions in 1 country.

Yangyang LiuSchool of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China.
Jiyun HanSchool of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China.
Tongxin KongSchool of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China.
Nannan XiaoSchool of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China.
Qinglin MeiMOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China.
Juntao LiuSchool of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China.ORCID 0000-0002-7296-906X
Shandong University · CNTsinghua University · CN

Funding

National Key Research and Development Program of China 2020YFA0712400National Natural Science Foundation of China 62272268
6 · The paper itself

Abstract

backgroundCancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes.

findingsWe present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis.

conclusionsThe new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

Indexed as

GenomicsNeoplasmsHumansMutationOncogenesReproducibility of Resultscancer genomicsdriver genesmultiomics in cancermutated gene pairs

Identifiers

PMID38091511
PMCPMC10716827
OpenAlexW4389655544

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.