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Items: 1 to 20 of 81

1.

HENA, heterogeneous network-based data set for Alzheimer's disease.

Sügis E, Dauvillier J, Leontjeva A, Adler P, Hindie V, Moncion T, Collura V, Daudin R, Loe-Mie Y, Herault Y, Lambert JC, Hermjakob H, Pupko T, Rain JC, Xenarios I, Vilo J, Simonneau M, Peterson H.

Sci Data. 2019 Aug 14;6(1):151. doi: 10.1038/s41597-019-0152-0.

2.

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).

Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, Vilo J.

Nucleic Acids Res. 2019 Jul 2;47(W1):W191-W198. doi: 10.1093/nar/gkz369.

3.

An exploratory phenome wide association study linking asthma and liver disease genetic variants to electronic health records from the Estonian Biobank.

James G, Reisberg S, Lepik K, Galwey N, Avillach P, Kolberg L, Mägi R, Esko T, Alexander M, Waterworth D, Loomis AK, Vilo J.

PLoS One. 2019 Apr 12;14(4):e0215026. doi: 10.1371/journal.pone.0215026. eCollection 2019.

4.

Comparison of variation in frequency for SNPs associated with asthma or liver disease between Estonia, HapMap populations and the 1000 genome project populations.

Reisberg S, Galwey N, Avillach P, Sahlqvist AS, Kolberg L, Mägi R, Esko T, Vilo J, James G.

Int J Immunogenet. 2019 Apr;46(2):49-58. doi: 10.1111/iji.12413. Epub 2019 Jan 19.

PMID:
30659741
5.

funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data.

Kolberg L, Kuzmin I, Adler P, Vilo J, Peterson H.

BMC Genomics. 2018 Nov 14;19(1):817. doi: 10.1186/s12864-018-5176-x.

6.

Genetic variation in the Estonian population: pharmacogenomics study of adverse drug effects using electronic health records.

Tasa T, Krebs K, Kals M, Mägi R, Lauschke VM, Haller T, Puurand T, Remm M, Esko T, Metspalu A, Vilo J, Milani L.

Eur J Hum Genet. 2019 Mar;27(3):442-454. doi: 10.1038/s41431-018-0300-6. Epub 2018 Nov 12.

7.

Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions.

Reisberg S, Krebs K, Lepamets M, Kals M, Mägi R, Metsalu K, Lauschke VM, Vilo J, Milani L.

Genet Med. 2019 Jun;21(6):1345-1354. doi: 10.1038/s41436-018-0337-5. Epub 2018 Oct 16.

PMID:
30327539
8.

Validating Fitbit Zip for monitoring physical activity of children in school: a cross-sectional study.

Mooses K, Oja M, Reisberg S, Vilo J, Kull M.

BMC Public Health. 2018 Jul 11;18(1):858. doi: 10.1186/s12889-018-5752-7.

9.

Prostaglandin D2 Receptor DP1 Antibodies Predict Vaccine-induced and Spontaneous Narcolepsy Type 1: Large-scale Study of Antibody Profiling.

Sadam H, Pihlak A, Kivil A, Pihelgas S, Jaago M, Adler P, Vilo J, Vapalahti O, Neuman T, Lindholm D, Partinen M, Vaheri A, Palm K.

EBioMedicine. 2018 Mar;29:47-59. doi: 10.1016/j.ebiom.2018.01.043. Epub 2018 Feb 2.

10.

MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data.

Modhukur V, Iljasenko T, Metsalu T, Lokk K, Laisk-Podar T, Vilo J.

Epigenomics. 2018 Mar;10(3):277-288. doi: 10.2217/epi-2017-0118. Epub 2017 Dec 21.

11.

DosOpt: A Tool for Personalized Bayesian Dose Adjustment of Vancomycin in Neonates.

Tasa T, Metsvaht T, Kalamees R, Vilo J, Lutsar I.

Ther Drug Monit. 2017 Dec;39(6):604-613. doi: 10.1097/FTD.0000000000000456.

PMID:
29084032
12.

Autoantibody Repertoire in APECED Patients Targets Two Distinct Subgroups of Proteins.

Fishman D, Kisand K, Hertel C, Rothe M, Remm A, Pihlap M, Adler P, Vilo J, Peet A, Meloni A, Podkrajsek KT, Battelino T, Bruserud Ø, Wolff ASB, Husebye ES, Kluger N, Krohn K, Ranki A, Peterson H, Hayday A, Peterson P.

Front Immunol. 2017 Aug 16;8:976. doi: 10.3389/fimmu.2017.00976. eCollection 2017.

13.

Comparing distributions of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations.

Reisberg S, Iljasenko T, Läll K, Fischer K, Vilo J.

PLoS One. 2017 Jul 5;12(7):e0179238. doi: 10.1371/journal.pone.0179238. eCollection 2017.

14.

DNA methylation changes in endometrium and correlation with gene expression during the transition from pre-receptive to receptive phase.

Kukushkina V, Modhukur V, Suhorutšenko M, Peters M, Mägi R, Rahmioglu N, Velthut-Meikas A, Altmäe S, Esteban FJ, Vilo J, Zondervan K, Salumets A, Laisk-Podar T.

Sci Rep. 2017 Jun 20;7(1):3916. doi: 10.1038/s41598-017-03682-0.

15.

Erratum to: DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns.

Lokk K, Modhukur V, Rajashekar B, Märtens K, Mägi R, Kolde R, Koltšina M, Nilsson TK, Vilo J, Salumets A, Tönisson N.

Genome Biol. 2016 Nov 1;17(1):224. No abstract available.

16.

seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data.

Kolde R, Märtens K, Lokk K, Laur S, Vilo J.

Bioinformatics. 2016 Sep 1;32(17):2604-10. doi: 10.1093/bioinformatics/btw304. Epub 2016 May 13.

17.

g:Profiler-a web server for functional interpretation of gene lists (2016 update).

Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, Vilo J.

Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199. Epub 2016 Apr 20.

18.

A Preclinical Model for ERα-Positive Breast Cancer Points to the Epithelial Microenvironment as Determinant of Luminal Phenotype and Hormone Response.

Sflomos G, Dormoy V, Metsalu T, Jeitziner R, Battista L, Scabia V, Raffoul W, Delaloye JF, Treboux A, Fiche M, Vilo J, Ayyanan A, Brisken C.

Cancer Cell. 2016 Mar 14;29(3):407-422. doi: 10.1016/j.ccell.2016.02.002. Epub 2016 Mar 3.

19.

GOsummaries: an R Package for Visual Functional Annotation of Experimental Data.

Kolde R, Vilo J.

F1000Res. 2015 Aug 18;4:574. doi: 10.12688/f1000research.6925.1. eCollection 2015.

20.

Age-related profiling of DNA methylation in CD8+ T cells reveals changes in immune response and transcriptional regulator genes.

Tserel L, Kolde R, Limbach M, Tretyakov K, Kasela S, Kisand K, Saare M, Vilo J, Metspalu A, Milani L, Peterson P.

Sci Rep. 2015 Aug 19;5:13107. doi: 10.1038/srep13107.

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