Evidence map›Paper›PMID 31874600›Full record

ArticleBMC bioinformatics2019

DNLC: differential network local consistency analysis.

Jianwei Lu, Yao Lu, Yusheng Ding, Qingyang Xiao, Linqing Liu, Qingpo Cai, Yunchuan Kong, Yun Bai, Tianwei Yu

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In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jianwei LuSchool of Software Engineering, Tongji University, Shanghai, China.
Yao LuSchool of Software Engineering, Tongji University, Shanghai, China.
Yusheng DingSchool of Software Engineering, Tongji University, Shanghai, China.
Qingyang XiaoDepartment of Environmental Health, Emory University, Atlanta, GA, USA.
Linqing LiuSchool of Software Engineering, Tongji University, Shanghai, China.
Qingpo CaiDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
Yunchuan KongDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
Yun BaiDepartment of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Georgia Campus, Suwanee, GA, USA. yunba@pcom.edu.
Tianwei YuDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA. tianwei.yu@emory.edu.ORCID http://orcid.org/0000-0003-2502-1628

Funding

Bayesian Network Biomarker Selection in Metabolomics DataR01GM124061 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KANG, JIAN · 2017 to 2020
$1.4M
Mechanisms of calcineurin isoform-specifics signalingR15GM113120 · NIGMS · PHILADELPHIA COLLEGE OF OSTEOPATHIC MED · PI BAI, YUN · 2015 to 2015
$390k
NIGMS NIH HHS R15 GM113120NIH HHS R01GM124061NIH HHS R15GM113120
6 · The paper itself

Abstract

backgroundThe biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages.

resultsIn this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible.

conclusionsThe new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

Indexed as

Gene Regulatory NetworksAlgorithmsGene Expression ProfilingHumansSoftwareBiological networkGene expressionLocal Moran’s I

Identifiers

PMID31874600
PMCPMC6929334

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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.