Evidence map›Paper›PMID 24982873›Full record

ArticleBioMed research international2014

Simulated annealing based algorithm for identifying mutated driver pathways in cancer.

Hai-Tao Li, Yu-Lang Zhang, Chun-Hou Zheng, Hong-Qiang Wang

Abstract read
In one paragraph

Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019
    Article
  4. 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

4 authors.

Hai-Tao LiCollege of Information and Communication Technology, Qufu Normal University, Rizhao 276826, China.
Yu-Lang ZhangCollege of Jia Sixie Agriculture, Weifang University of Science and Technology, Shouguang 262700, China.
Chun-Hou ZhengCollege of Information and Communication Technology, Qufu Normal University, Rizhao 276826, China ; College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China.
Hong-Qiang WangIntelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random "passenger mutations." In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice.

Indexed as

AlgorithmsComputer SimulationGenes, NeoplasmHumansLung NeoplasmsMarkov ChainsMonte Carlo MethodMutationNeoplasmsSignal TransductionTime FactorsTOR Serine-Threonine KinasesTOR Serine-Threonine Kinases

Identifiers

PMID24982873
PMCPMC4058194

What OpenQuestion holds

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LicenceCC BY
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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.