Evidence map›Paper›PMID 31888692›Full record

ArticleBMC medical genomics2019

A new method for mining information of co-expression network based on multi-cancers integrated data.

Mi-Xiao Hou, Ying-Lian Gao, Jin-Xing Liu, Junliang Shang, Rong Zhu, Sha-Sha Yuan

Abstract read
In one paragraph

Article in BMC medical genomics, 2019. 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
–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

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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.

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4 · The record

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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.

Mi-Xiao HouSchool of Information Science and Engineering, Qufu Normal University, Rizhao, China.
Ying-Lian GaoQufu Normal University Library, Qufu Normal University, Rizhao, China. yinliangao@126.com.
Jin-Xing LiuSchool of Information Science and Engineering, Qufu Normal University, Rizhao, China. sdcavell@126.com.ORCID 0000-0001-6104-2149
Junliang ShangSchool of Information Science and Engineering, Qufu Normal University, Rizhao, China.
Rong ZhuSchool of Information Science and Engineering, Qufu Normal University, Rizhao, China.
Sha-Sha YuanSchool of Information Science and Engineering, Qufu Normal University, Rizhao, China.

Funding

National Natural Science Foundation of China 61572284National Natural Science Foundation of China 61701279National Natural Science Foundation of China 61702299National Natural Science Foundation of China 61872220the Co-Innovation Center for Information Supply & Assurance Technology, Anhui University ADXXBZ201704
6 · The paper itself

Abstract

backgroundGene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction.

methodsIn this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes.

resultsWe uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation.

conclusionsThe results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

Indexed as

Databases, GeneticData MiningGene Regulatory NetworksGenes, NeoplasmHumansNeoplasmsAbnormally expressed genesGene co-expression networkMutual informationPearson correlation coefficientTCGA

Identifiers

PMID31888692
PMCPMC6936053

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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.