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Items: 1 to 20 of 28

1.

Huntington's disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels.

Horvath S, Langfelder P, Kwak S, Aaronson J, Rosinski J, Vogt TF, Eszes M, Faull RL, Curtis MA, Waldvogel HJ, Choi OW, Tung S, Vinters HV, Coppola G, Yang XW.

Aging (Albany NY). 2016 Jul;8(7):1485-512. doi: 10.18632/aging.101005.

2.

Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice.

Langfelder P, Cantle JP, Chatzopoulou D, Wang N, Gao F, Al-Ramahi I, Lu XH, Ramos EM, El-Zein K, Zhao Y, Deverasetty S, Tebbe A, Schaab C, Lavery DJ, Howland D, Kwak S, Botas J, Aaronson JS, Rosinski J, Coppola G, Horvath S, Yang XW.

Nat Neurosci. 2016 Apr;19(4):623-33. doi: 10.1038/nn.4256.

PMID:
26900923
3.

A Systems-Level Analysis of the Peripheral Nerve Intrinsic Axonal Growth Program.

Chandran V, Coppola G, Nawabi H, Omura T, Versano R, Huebner EA, Zhang A, Costigan M, Yekkirala A, Barrett L, Blesch A, Michaelevski I, Davis-Turak J, Gao F, Langfelder P, Horvath S, He Z, Benowitz L, Fainzilber M, Tuszynski M, Woolf CJ, Geschwind DH.

Neuron. 2016 Mar 2;89(5):956-70. doi: 10.1016/j.neuron.2016.01.034.

PMID:
26898779
4.

Genomic profiles and predictors of early allograft dysfunction after human liver transplantation.

Kurian SM, Fouraschen SM, Langfelder P, Horvath S, Shaked A, Salomon DR, Olthoff KM.

Am J Transplant. 2015 Jun;15(6):1605-14. doi: 10.1111/ajt.13145.

5.

Molecular classifiers for acute kidney transplant rejection in peripheral blood by whole genome gene expression profiling.

Kurian SM, Williams AN, Gelbart T, Campbell D, Mondala TS, Head SR, Horvath S, Gaber L, Thompson R, Whisenant T, Lin W, Langfelder P, Robison EH, Schaffer RL, Fisher JS, Friedewald J, Flechner SM, Chan LK, Wiseman AC, Shidban H, Mendez R, Heilman R, Abecassis MM, Marsh CL, Salomon DR.

Am J Transplant. 2014 May;14(5):1164-72. doi: 10.1111/ajt.12671.

6.

Transcriptome analysis of HIV-infected peripheral blood monocytes: gene transcripts and networks associated with neurocognitive functioning.

Levine AJ, Horvath S, Miller EN, Singer EJ, Shapshak P, Baldwin GC, Martínez-Maza O, Witt MD, Langfelder P.

J Neuroimmunol. 2013 Dec 15;265(1-2):96-105. doi: 10.1016/j.jneuroim.2013.09.016.

7.

When is hub gene selection better than standard meta-analysis?

Langfelder P, Mischel PS, Horvath S.

PLoS One. 2013 Apr 17;8(4):e61505. doi: 10.1371/journal.pone.0061505.

8.

Cluster and propensity based approximation of a network.

Ranola JM, Langfelder P, Lange K, Horvath S.

BMC Syst Biol. 2013 Mar 14;7:21. doi: 10.1186/1752-0509-7-21.

9.

Random generalized linear model: a highly accurate and interpretable ensemble predictor.

Song L, Langfelder P, Horvath S.

BMC Bioinformatics. 2013 Jan 16;14:5. doi: 10.1186/1471-2105-14-5.

10.

Comparison of co-expression measures: mutual information, correlation, and model based indices.

Song L, Langfelder P, Horvath S.

BMC Bioinformatics. 2012 Dec 9;13:328. doi: 10.1186/1471-2105-13-328.

11.

Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects.

van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, de Kovel CG, Janson E, Strengman E, Langfelder P, Kahn RS, van den Berg LH, Horvath S, Ophoff RA.

BMC Genomics. 2012 Nov 17;13:636. doi: 10.1186/1471-2164-13-636.

12.

Fast R Functions for Robust Correlations and Hierarchical Clustering.

Langfelder P, Horvath S.

J Stat Softw. 2012 Mar;46(11). pii: i11.

13.

Aging effects on DNA methylation modules in human brain and blood tissue.

Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K, van den Berg LH, Ophoff RA.

Genome Biol. 2012 Oct 3;13(10):R97. doi: 10.1186/gb-2012-13-10-r97.

14.

Network methods for describing sample relationships in genomic datasets: application to Huntington's disease.

Oldham MC, Langfelder P, Horvath S.

BMC Syst Biol. 2012 Jun 12;6:63. doi: 10.1186/1752-0509-6-63.

15.

Strategies for aggregating gene expression data: the collapseRows R function.

Miller JA, Cai C, Langfelder P, Geschwind DH, Kurian SM, Salomon DR, Horvath S.

BMC Bioinformatics. 2011 Aug 4;12:322. doi: 10.1186/1471-2105-12-322.

16.

A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models.

Langfelder P, Castellani LW, Zhou Z, Paul E, Davis R, Schadt EE, Lusis AJ, Horvath S, Mehrabian M.

Biochim Biophys Acta. 2012 Mar;1821(3):435-47. doi: 10.1016/j.bbalip.2011.07.014.

17.

Clinical and plasma proteomic markers correlating with chronic kidney disease after liver transplantation.

Levitsky J, Salomon DR, Abecassis M, Langfelder P, Horvath S, Friedewald J, Wang E, Kurian SM, Mondala T, Gil S, McDade R, Ballard K, Gallon L.

Am J Transplant. 2011 Sep;11(9):1972-8. doi: 10.1111/j.1600-6143.2011.03669.x.

18.

Gene networks associated with conditional fear in mice identified using a systems genetics approach.

Park CC, Gale GD, de Jong S, Ghazalpour A, Bennett BJ, Farber CR, Langfelder P, Lin A, Khan AH, Eskin E, Horvath S, Lusis AJ, Ophoff RA, Smith DJ.

BMC Syst Biol. 2011 Mar 16;5:43. doi: 10.1186/1752-0509-5-43.

19.

Is my network module preserved and reproducible?

Langfelder P, Luo R, Oldham MC, Horvath S.

PLoS Comput Biol. 2011 Jan 20;7(1):e1001057. doi: 10.1371/journal.pcbi.1001057.

20.

Gene coexpression network topology of cardiac development, hypertrophy, and failure.

Dewey FE, Perez MV, Wheeler MT, Watt C, Spin J, Langfelder P, Horvath S, Hannenhalli S, Cappola TP, Ashley EA.

Circ Cardiovasc Genet. 2011 Feb;4(1):26-35. doi: 10.1161/CIRCGENETICS.110.941757.

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