Evidence map›Paper›PMID 33203358›Full record

ArticleBMC bioinformatics2020

LC-N2G: a local consistency approach for nutrigenomics data analysis.

Xiangnan Xu, Samantha M Solon-Biet, Alistair Senior, David Raubenheimer, Stephen J Simpson, Luigi Fontana, Samuel Mueller, Jean Y H Yang

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Article in BMC bioinformatics, 2020. 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
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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

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xiangnan XuSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.
Samantha M Solon-BietCharles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Alistair SeniorCharles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
David RaubenheimerCharles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Stephen J SimpsonCharles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Luigi FontanaCharles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Samuel MuellerDepartment of Mathematics and Statistics, Macquarie University, Sydney, NSW, 2109, Australia.
Jean Y H YangSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia. jean.yang@sydney.edu.au.ORCID http://orcid.org/0000-0002-5271-2603

Funding

Australia NHMRC Career Developmental Fellowship APP1111338Australian Research Council Discovery Project grant DP170100654
6 · The paper itself

Abstract

backgroundNutrigenomics aims at understanding the interaction between nutrition and gene information. Due to the complex interactions of nutrients and genes, their relationship exhibits non-linearity. One of the most effective and efficient methods to explore their relationship is the nutritional geometry framework which fits a response surface for the gene expression over two prespecified nutrition variables. However, when the number of nutrients involved is large, it is challenging to find combinations of informative nutrients with respect to a certain gene and to test whether the relationship is stronger than chance. Methods for identifying informative combinations are essential to understanding the relationship between nutrients and genes.

resultsWe introduce Local Consistency Nutrition to Graphics (LC-N2G), a novel approach for ranking and identifying combinations of nutrients with gene expression. In LC-N2G, we first propose a model-free quantity called Local Consistency statistic to measure whether there is non-random relationship between combinations of nutrients and gene expression measurements based on (1) the similarity between samples in the nutrient space and (2) their difference in gene expression. Then combinations with small LC are selected and a permutation test is performed to evaluate their significance. Finally, the response surfaces are generated for the subset of significant relationships. Evaluation on simulated data and real data shows the LC-N2G can accurately find combinations that are correlated with gene expression.

conclusionThe LC-N2G is practically powerful for identifying the informative nutrition variables correlated with gene expression. Therefore, LC-N2G is important in the area of nutrigenomics for understanding the relationship between nutrition and gene expression information.

Indexed as

AlgorithmsData AnalysisNutrigenomicsAnimal Nutritional Physiological PhenomenaAnimalsComputer SimulationGene Expression RegulationMiceNonlinear DynamicsGene expressionLocal consistencyNutrigenmoicsNutrition

Identifiers

PMID33203358
PMCPMC7672905

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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.