{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:52:29Z","timestamp":1740135149915,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T00:00:00Z","timestamp":1605571200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T00:00:00Z","timestamp":1605571200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Research Council Discovery Project grant","award":["DP170100654"],"award-info":[{"award-number":["DP170100654"]}]},{"name":"Australia NHMRC Career Developmental Fellowship","award":["APP1111338"],"award-info":[{"award-number":["APP1111338"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Nutrigenomics 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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We 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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>The 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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-020-03861-3","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T18:02:58Z","timestamp":1605636178000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LC-N2G: a local consistency approach for nutrigenomics data analysis"],"prefix":"10.1186","volume":"21","author":[{"given":"Xiangnan","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samantha M.","family":"Solon-Biet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alistair","family":"Senior","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Raubenheimer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen J.","family":"Simpson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Fontana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Mueller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-2603","authenticated-orcid":false,"given":"Jean Y. H.","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,17]]},"reference":[{"key":"3861_CR1","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1038\/nature14190","volume":"517","author":"A Efeyan","year":"2015","unstructured":"Efeyan A, Comb WC, Sabatini DM. Nutrient-sensing mechanisms and pathways. Nature. 2015;517:302\u201310.","journal-title":"Nature"},{"key":"3861_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1159\/000324175","volume":"4","author":"CD Davis","year":"2011","unstructured":"Davis CD, Milner JA. Nutrigenomics, vitamin d and cancer prevention. J Nutrigenet Nutrigenomics. 2011;4:1\u201311.","journal-title":"J Nutrigenet Nutrigenomics"},{"key":"3861_CR3","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1038\/35057062","volume":"409","author":"ES Lander","year":"2001","unstructured":"Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W. Initial sequencing and analysis of the human genome. Nature. 2001;409:860\u2013921.","journal-title":"Nature"},{"key":"3861_CR4","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nature03001","volume":"431","author":"IHGS Consortium","year":"2004","unstructured":"Consortium IHGS. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931\u201345.","journal-title":"Nature"},{"key":"3861_CR5","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.jff.2018.01.021","volume":"42","author":"M Roberto","year":"2018","unstructured":"Roberto M, Guillermo R, Alberto D. Data mining of nutrigenomics experiments: identification of a cancer protective gene signature. J Funct Foods. 2018;42:380\u20136.","journal-title":"J Funct Foods"},{"key":"3861_CR6","first-page":"097","volume":"2019","author":"M Roberto","year":"2019","unstructured":"Roberto M, Guillermo RMJ, Alberto D. Nutrigenomedb: a nutrigenomics exploratory and analytical platform. Database. 2019;2019:097.","journal-title":"Database"},{"key":"3861_CR7","first-page":"583","volume":"115","author":"MN Mead","year":"2007","unstructured":"Mead MN. Nutrigenomics: the genome-food interface. Environ Health Perspect. 2007;115:583\u20139.","journal-title":"Environ Health Perspect"},{"key":"3861_CR8","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.cmet.2017.02.001","volume":"25","author":"F Leulier","year":"2017","unstructured":"Leulier F, MacNeil LT, Lee W, Rawls JF, Cani PD, Schwarzer M, Zhao L, Simpson SJ. Integrative physiology: at the crossroads of nutrition, microbiota, animal physiology and human health. Cell Metab. 2017;25:522\u201334.","journal-title":"Cell Metab"},{"key":"3861_CR9","doi-asserted-by":"publisher","DOI":"10.1515\/9781400842803","volume-title":"The nature of nutrition: a unifying framework from animal adaptation to human obesity","author":"SJ Simpson","year":"2012","unstructured":"Simpson SJ, Raubenheimer D. The nature of nutrition: a unifying framework from animal adaptation to human obesity. Princeton, NJ: Princeton University Press; 2012."},{"key":"3861_CR10","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1146\/annurev-nutr-071715-051118","volume":"36","author":"D Raubenheimer","year":"2016","unstructured":"Raubenheimer D, Simpson SJ. Nutritional ecology and human health. Annu Rev Nutr. 2016;36:603\u201326.","journal-title":"Annu Rev Nutr"},{"key":"3861_CR11","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.cmet.2011.06.013","volume":"14","author":"MDW Piper","year":"2011","unstructured":"Piper MDW, Partridge L, Raubenheimer D, Simpson SJ. Dietary restriction and ageing: a unifying perspective. Cell Metab. 2011;14:154\u201360.","journal-title":"Cell Metab"},{"key":"3861_CR12","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1111\/j.1365-2435.2010.01766.x","volume":"25","author":"SC Cotter","year":"2011","unstructured":"Cotter SC, Simpson SJ, Raubenheimer D, Wilson K. Macronutrient balance mediates trade-offs between immune function and life history traits. Funct Ecol. 2011;25:186\u201398.","journal-title":"Funct Ecol"},{"key":"3861_CR13","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1073\/pnas.0710787105","volume":"105","author":"KP Lee","year":"2008","unstructured":"Lee KP, Simpson SJ, Clissold FJ, Brooks R, Ballard JWO, Taylor PW, Soran N, Raubenheimer D. Lifespan and reproduction in drosophila: new insights from nutritional geometry. Proc Natl Acad Sci USA. 2008;105:2498\u2013503.","journal-title":"Proc Natl Acad Sci USA"},{"key":"3861_CR14","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.ibmb.2019.04.009","volume":"109","author":"CC Cotter","year":"2019","unstructured":"Cotter CC, Reavey CE, Tummala Y, Randall JL, Holdbrook R, Ponton F, Simpson SJ, Smith JA, Wilson K. Diet modulates the relationship between immune gene expression and functional immune responses. Insect Biochem Mol Biol. 2019;109:129\u201341.","journal-title":"Insect Biochem Mol Biol"},{"key":"3861_CR15","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.cmet.2016.09.001","volume":"24","author":"SM Solon-Biet","year":"2016","unstructured":"Solon-Biet SM, Cogger VC, Pulpitel T, Heblinski M, Wahl D, McMahon AC, Warren A, Durrant-Whyte J, Walters KA, Krycer JR, Ponton F, Gokarn R, Wali JA, Ruohonen K, Conigrave AD, James DE, Raubenheimer D, Morrison CD, Le Couteur DG, Simpson SJ. Defining the nutritional and metabolic context of fgf21 using the geometric framework. Cell Metab. 2016;24:555\u201365.","journal-title":"Cell Metab"},{"key":"3861_CR16","doi-asserted-by":"publisher","first-page":"217","DOI":"10.3233\/NHA-170027","volume":"4","author":"SJ Simpson","year":"2017","unstructured":"Simpson SJ, Le Couteur DG, James DE, George J, Gunton JE, Solon-Biet SM, Raubenheimer D. The geometric framework for nutrition as a tool in precision medicine. Nutr Healthy Aging. 2017;4:217\u201366.","journal-title":"Nutr Healthy Aging"},{"key":"3861_CR17","doi-asserted-by":"crossref","unstructured":"Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4.","DOI":"10.2202\/1544-6115.1128"},{"key":"3861_CR18","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1007\/s11745-011-3596-3","volume":"46","author":"B Ason","year":"2011","unstructured":"Ason B, Castro-Perez J, Tep S, Stefanni A, Tadin-Strapps M, Roddy T, Hankemeier T, Hubbard B, Sachs AB, Flanagan WM, Kuklin NA, Mitnaul LJ. Apobsirna-induced liver steatosis is resistant to clearance by the loss of fatty acid transport protein 5 (fatp5). Lipids. 2011;46:991\u20131003.","journal-title":"Lipids"},{"key":"3861_CR19","doi-asserted-by":"publisher","first-page":"23097","DOI":"10.1038\/srep23097","volume":"6","author":"Y Watanabe","year":"2016","unstructured":"Watanabe Y, Nagai Y, Honda H, Okamoto N, Yamamoto S, Hamashima T, Ishii Y, Tanaka M, Suganami T, Sasahara M, Miyake K, Takatsu K. Isoliquiritigenin attenuates adipose tissue inflammation in vitro and adipose tissue fibrosis through inhibition of innate immune responses in mice. Sci Rep. 2016;6:23097.","journal-title":"Sci Rep"},{"key":"3861_CR20","first-page":"855","volume":"299","author":"F Ge","year":"2010","unstructured":"Ge F, Zhou S, Hu C, Lobdell H, Berk PD. Insulin- and leptin-regulated fatty acid uptake plays a key causal role in hepatic steatosis in mice with intact leptin signaling but not in ob\/ob or db\/db mice. Am J Physiol. 2010;299:855\u201366.","journal-title":"Am J Physiol"},{"key":"3861_CR21","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1161\/CIRCULATIONAHA.116.022668","volume":"134","author":"M Cl\u00e9ment","year":"2016","unstructured":"Cl\u00e9ment M, Basatemur G, Masters L, Baker L, Bruneval P, Iwawaki T, Kneilling M, Yamasaki S, Goodall J, Mallat Z. Necrotic cell sensor clec4e promotes a pro-atherogenic macrophage phenotype through activation of the unfolded protein response. Circulation. 2016;134:1039\u201351.","journal-title":"Circulation"},{"key":"3861_CR22","first-page":"513","volume-title":"NIPS\u201904 proceedings of the 17th international conference on neural information processing systems","author":"J Goldberger","year":"2004","unstructured":"Goldberger J, Roweis S, Hinton G, Salakhutdinov R. Neighbourhood components analysis. In: Saul LK, Weiss Y, Bottou L, editors. NIPS\u201904 proceedings of the 17th international conference on neural information processing systems. Cambridge: MIT Press; 2004. p. 513\u201320."},{"key":"3861_CR23","first-page":"849","volume-title":"NIPS\u201901 proceedings of the 14th international conference on neural information processing systems: natural and synthetic","author":"YN Andrew","year":"2001","unstructured":"Andrew YN, Michael IJ, Yair W. On spectral clustering: analysis and an algorithm. In: Dietterich TG, Becker S, Ghahramani Z, editors. NIPS\u201901 proceedings of the 14th international conference on neural information processing systems: natural and synthetic. Cambridge: MIT Press; 2001. p. 849\u201356."},{"key":"3861_CR24","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Luxburg","year":"2007","unstructured":"Luxburg U. A tutorial on spectral clustering. Stat Comput. 2007;17:395\u2013416.","journal-title":"Stat Comput"},{"key":"3861_CR25","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","volume":"58","author":"S Wold","year":"2001","unstructured":"Wold S, Sj\u00f6str\u00f6m M, Eriksson L. Pls-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst. 2001;58:109\u201330.","journal-title":"Chemometr Intell Lab Syst"},{"key":"3861_CR26","volume-title":"Genetic algorithm in search, optimization, and machine learning","author":"DE Goldberg","year":"1989","unstructured":"Goldberg DE. Genetic algorithm in search, optimization, and machine learning. Boston: Addison-Wesley; 1989."},{"key":"3861_CR27","doi-asserted-by":"publisher","first-page":"3481","DOI":"10.1073\/pnas.1422041112","volume":"112","author":"SM Solon-Biet","year":"2015","unstructured":"Solon-Biet SM, Walters KA, Simanainen U, McMahon AC, Ruohonen K, Ballard JWO, Raubenheimer D, Handelsman DJ, Le Couteur DG, Simpson SJ. Macronutrient balance, reproductive function and lifespan in aging mice. Proc Natl Acad Sci USA. 2015;112:3481\u20136.","journal-title":"Proc Natl Acad Sci USA"},{"key":"3861_CR28","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1007\/s00018-015-2120-y","volume":"73","author":"DG Le Couteur","year":"2016","unstructured":"Le Couteur DG, Solon-Biet SM, Cogger VC, Mitchell SJ, Senior A, de Cabo R, Raubenheimer D, Simpson SJ. The impact of low-protein high-carbohydrate diets on aging and lifespan. Cell Mol Life Sci. 2016;73:1237\u201352.","journal-title":"Cell Mol Life Sci"},{"key":"3861_CR29","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","volume":"19","author":"BM Bolstad","year":"2003","unstructured":"Bolstad BM, Irizarry RA, \u00c5strand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics. 2003;19:185\u201393.","journal-title":"Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03861-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-03861-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03861-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T18:04:31Z","timestamp":1605636271000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03861-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,17]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3861"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03861-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2020,11,17]]},"assertion":[{"value":"27 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not Applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"530"}}