20 results found
Evangelou E, Warren HR, Mosen-Ansorena D, et al., 2018, Publisher Correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits., Nat Genet, Vol: 50
In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.
Mustafa R, Ghanbari M, Evangelou M, et al., 2018, An Enrichment Analysis for Cardiometabolic Traits Suggests Non-Random Assignment of Genes to microRNAs., Int J Mol Sci, Vol: 19
MicroRNAs (miRNAs) regulate the expression of the majority of genes. However, it is not known whether they regulate genes in random or are organized according to their function. To this end, we chose cardiometabolic disorders as an example and investigated whether genes associated with cardiometabolic disorders are regulated by a random set of miRNAs or a limited number of them. Single-nucleotide polymorphisms (SNPs) reaching genome-wide level significance were retrieved from most recent genome-wide association studies on cardiometabolic traits, which were cross-referenced with Ensembl to identify related genes and combined with miRNA target prediction databases (TargetScan, miRTarBase, or miRecords) to identify miRNAs that regulate them. We retrieved 520 SNPs, of which 355 were intragenic, corresponding to 304 genes. While we found a higher proportion of genes reported from all GWAS that were predicted targets for miRNAs in comparison to all protein-coding genes (75.1%), the proportion was even higher for cardiometabolic genes (80.6%). Enrichment analysis was performed within each database. We found that cardiometabolic genes were over-represented in target genes for 29 miRNAs (based on TargetScan) and 3 miRNAs (miR-181a, miR-302d and miR-372) (based on miRecords) after Benjamini-Hochberg correction for multiple testing. Our work provides evidence for non-random assignment of genes to miRNAs and supports the idea that miRNAs regulate sets of genes that are functionally related.
Evangelou E, Warren HR, Mosen-Ansorena D, et al., 2018, Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits, NATURE GENETICS, Vol: 50, Pages: 1412-+, ISSN: 1061-4036
Broc C, Evangelou M, Truong T, et al., 2018, Investigating Gene- and Pathway-environment Interaction analysis approaches, JOURNAL OF THE SFDS, Vol: 159, Pages: 56-83, ISSN: 2102-6238
Schon C, Adams N, Evangelou M, 2017, Clustering and Monitoring Edge Behaviour in Enterprise Network Traffic, 15th IEEE International Conference on Intelligence and Security Informatics - Security and Big Data (ISI), Publisher: IEEE, Pages: 31-36
Nasser S, Lazaridis A, Evangelou M, et al., 2016, Correlation of pre-operative CT findings with surgical & histological tumor dissemination patterns at cytoreduction for primary advanced and relapsed epithelial ovarian cancer: A retrospective evaluation, GYNECOLOGIC ONCOLOGY, Vol: 143, Pages: 264-269, ISSN: 0090-8258
Larsen E, Truong T, Evangelou M, 2016, Exploring GenexEnvironment Interactions through Pathway Analysis, Annual Meeting of the International-Genetic-Epidemiology-Society, Publisher: WILEY-BLACKWELL, Pages: 648-649, ISSN: 0741-0395
Todd JA, Evangelou M, Cutler AJ, et al., 2016, Regulatory T Cell Responses in Participants with Type 1 Diabetes after a Single Dose of Interleukin-2: A Non-Randomised, Open Label, Adaptive Dose-Finding Trial, PLOS MEDICINE, Vol: 13, ISSN: 1549-1676
Whitehouse M, Evangelou M, Adams NM, 2016, Activity-based temporal anomaly detection in enterprise-cyber security, 14th IEEE International Conference on Intelligence and Security Informatics - Cybersecurity and Big Data (IEEE ISI), Publisher: IEEE, Pages: 248-250
Evangelou M, Adams NM, 2016, Predictability of NetFlow data, 14th IEEE International Conference on Intelligence and Security Informatics - Cybersecurity and Big Data (IEEE ISI), Publisher: IEEE, Pages: 67-72
Gibberd AJ, Evangelou M, Nelson JDB, 2016, The Time-Varying Dependency Patterns of NetFlow Statistics, 16th IEEE International Conference on Data Mining (ICDM), Publisher: IEEE, Pages: 288-294, ISSN: 2375-9232
Dopico XC, Evangelou M, Ferreira RC, et al., 2015, Widespread seasonal gene expression reveals annual differences in human immunity and physiology, NATURE COMMUNICATIONS, Vol: 6, ISSN: 2041-1723
Heywood J, Evangelou M, Goymer D, et al., 2015, Effective recruitment of participants to a phase I study using the internet and publicity releases through charities and patient organisations: analysis of the adaptive study of IL-2 dose on regulatory T cells in type 1 diabetes (DILT1D), TRIALS, Vol: 16, ISSN: 1745-6215
Truman LA, Pekalski ML, Kareclas P, et al., 2015, Protocol of the adaptive study of IL-2 dose frequency on regulatory T cells in type 1 diabetes (DILfrequency): a mechanistic, non-randomised, repeat dose, open-label, response-adaptive study, BMJ OPEN, Vol: 5, ISSN: 2044-6055
Evangelou M, Smyth DJ, Fortune MD, et al., 2014, A Method for Gene-Based Pathway Analysis Using Genomewide Association Study Summary Statistics Reveals Nine New Type 1 Diabetes Associations, GENETIC EPIDEMIOLOGY, Vol: 38, Pages: 661-670, ISSN: 0741-0395
Evangelou M, Dudbridge F, Wernisch L, 2014, Two novel pathway analysis methods based on a hierarchical model, BIOINFORMATICS, Vol: 30, Pages: 690-697, ISSN: 1367-4803
Evangelou M, Rendon A, Ouwehand WH, et al., 2012, Comparison of Methods for Competitive Tests of Pathway Analysis, PLOS ONE, Vol: 7, ISSN: 1932-6203
Evangelou M, Wernisch L, Dudbridge F, 2012, Comparison of Methods for Enrichment Tests in Pathway Analysis, 20th Annual Meeting of the International-Genetic-Epidemiology-Society (IGES), Publisher: WILEY-BLACKWELL, Pages: 150-151, ISSN: 0741-0395
Evangelou M, Dudbridge F, Wernisch L, 2012, Bayesian Hierarchical Modelling of SNPs and Pathways for Identifying Associated Pathways, 20th Annual Meeting of the International-Genetic-Epidemiology-Society (IGES), Publisher: WILEY-BLACKWELL, Pages: 150-150, ISSN: 0741-0395
Riddle-Workman E, Evangelou M, Adams N, Adaptive Anomaly Detection on Network Data Streams, IEEE Conference on Intelligence and Security Informatics (ISI) 2018, Publisher: IEEE
As the number of cyber-attacks increases, there hasbeen increasing emphasis on developing complementary methodsof detection to the existing signature-based approaches. This workbuilds upon a previously discovered persistent structure withinthe Los Alamos National Laboratory network data sources,to develop a regression based streaming anomaly detectionmechanism that can adapt to the network behaviour over time.The methodology has also been applied to a new data set of thesame network to assess the extent of its pertinence in time.
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