21 results found
Mustafa R, Ghanbari M, Evangelou M, et al., 2018, An enrichment analysis for cardiometabolic traits suggests non-random assignment of genes to microRNAs, International Journal of Molecular Sciences, Vol: 19, ISSN: 1422-0067
MicroRNAs (miRNAs) regulate the expression of 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, Publisher Correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018), Nature Genetics, Vol: 50, Pages: 1755-1755, ISSN: 1061-4036
Correction to: Nature Genetics https://doi.org/10.1038/s41588-018-0205-x, published online 17 September 2018.
Evangelou E, Elliott P, Tzoulaki I, et al., 2018, Genetic analysis of over one million people identifies 535 new loci associated with blood pressure traits, Nature Genetics, ISSN: 1061-4036
High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.
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.
Warren HR, Evangelou E, Mosen D, et al., 2018, GENETIC ANALYSIS OF OVER ONE MILLION PEOPLE IDENTIFIES 535 NOVEL LOCI ASSOCIATED WITH BLOOD PRESSURE AND RISK OF CARDIOVASCULAR DISEASE, 28th European Meeting of Hypertension and Cardiovascular Protection of the European-Society-of-Hypertension (ESH), Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: E229-E229, ISSN: 0263-6352
Broc C, Evangelou M, Truong T, et al., Investigating gene- and pathway-environment Interaction analysis approaches, Journal of the French Statistical Society, ISSN: 1962-5197
Pathway analysis can increase power to detect associations with a gene or a pathway by combining severalsignals at the single nucleotide polymorphism (SNP)-level into a single test. In this work, we propose to extend twowell-known self-contained methods, the Fisher’s method (FM) and the Adaptive Rank Truncated Product (ARTP)method to the analysis of gene-environment (GxE) interaction at the gene and pathway-level. It has been previouslysuggested that the permutation procedures that are usually used to derive the significance of these tests are notappropriate for the analysis of GxE interaction and should be replaced by a bootstrap approach. We analyse andcompare the performance of the extension of FM and ARTP using the permutation and the parametric bootstrapprocedure in simulation studies. We illustrate its application by analysing the interaction between night work andcircadian gene polymorphisms in the risk of breast cancer in a case-control study. The ARTP method, adapted for bothgene- and pathway-environment interactions, gives promising results and has been wrapped to the R package PIGEavailable on the CRAN.
Schon C, Adams NM, Evangelou M, Clustering and monitoring edge behaviour in enterprise network traffic, IEEE International Conference on Intelligence and Security Informatics, Publisher: IEEE
This paper takes an unsupervised learning approachfor monitoring edge activity within an enterprise computernetwork. Using NetFlow records, features are gathered acrossthe active connections (edges) in 15-minute time windows.Then, edges are grouped into clusters using the k-meansalgorithm. This process is repeated over contiguous windows.A series of informative indicators are derived by examining therelationship of edges with the observed cluster structure. Thisleads to an intuitive method for monitoring network behaviourand a temporal description of edge behaviour at global andlocal levels.
Evangelou M, Adams N, 2016, Predictability of NetFlow data, IEEE International Conference on Intelligence and Security Informatics, Publisher: IEEE
The behaviour of individual devices connected to anenterprise network can vary dramatically, as a device’s activitydepends on the user operating the device as well as on all behindthe scenes operations between the device and the network. Beingable to understand and predict a device’s behaviour in a networkcan work as the foundation of an anomaly detection framework,as devices may show abnormal activity as part of a cyber attack.The aim of this work is the construction of a predictive regressionmodel for a device’s behaviour at normal state. The behaviourof a device is presented by a quantitative response and modelledto depend on historic data recorded by NetFlow.
Whitehouse M, Evangelou M, Adams N, 2016, Activity-based temporal anomaly detection in enterprise-cyber security, IEEE International Big Data Analytics for Cybersecurity computing (BDAC'16) Workshop, IEEE International Conference on Intelligence and Security Informatics, Publisher: IEEE
Statistical anomaly detection is emerging as animportant complement to signature-based methods for enterprisenetwork defence. In this paper, we isolate a persistent structurein two different enterprise network data sources. This structureprovides the basis of a regression-based anomaly detectionmethod. The procedure is demonstrated on a large public domaindata set.
Todd J, 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: 1549-1277
BackgroundInterleukin-2 (IL-2) has an essential role in the expansion and function of CD4+ regulatory Tcells (Tregs). Tregs reduce tissue damage by limiting the immune response following infectionand regulate autoreactive CD4+ effector T cells (Teffs) to prevent autoimmune diseases,such as type 1 diabetes (T1D). Genetic susceptibility to T1D causes alterations inthe IL-2 pathway, a finding that supports Tregs as a cellular therapeutic target. Aldesleukin(Proleukin; recombinant human IL-2), which is administered at high doses to activate the immune system in cancer immunotherapy, is now being repositioned to treat inflammatoryand autoimmune disorders at lower doses by targeting Tregs.Methods and FindingsTo define the aldesleukin dose response for Tregs and to find doses that increase Tregsphysiologically for treatment of T1D, a statistical and systematic approach was taken byanalysing the pharmacokinetics and pharmacodynamics of single doses of subcutaneousaldesleukin in the Adaptive Study of IL-2 Dose on Regulatory T Cells in Type 1 Diabetes(DILT1D), a single centre, non-randomised, open label, adaptive dose-finding trial with 40adult participants with recently diagnosed T1D. The primary endpoint was the maximumpercentage increase in Tregs (defined as CD3+CD4+CD25highCD127low) from the baselinefrequency in each participant measured over the 7 d following treatment. There was an initiallearning phase with five pairs of participants, each pair receiving one of five preassignedsingle doses from 0.04 × 106 to 1.5 × 106 IU/m2, in order to model the doseresponsecurve. Results from each participant were then incorporated into interim statisticalmodelling to target the two doses most likely to induce 10% and 20% increases in Treg frequencies.Primary analysis of the evaluable population (n = 39) found that the optimaldoses of aldesleukin to induce 10% and 20% increases in Tregs were 0.101 × 106 IU/m2(standard error [SE] = 0.078, 95% CI = −0.052, 0.254
Larsen E, Truong T, Evangelou M, 2016, Exploring GenexEnvironment interactions through pathway analysis, Annual Meeting of the International-Genetic-Epidemiology-Society, Publisher: Wiley, Pages: 648-649, ISSN: 1098-2272
Gibberd AJ, Evangelou M, Nelson JDB, The time-varying dependency patterns of NetFlow statistics, IEEE International Conference on Data Mining Workshop Proceedings, Publisher: IEEE
We investigate where and how key dependencystructure between measures of network activity change through-out the course of daily activity. Our approach to data-mining isprobabilistic in nature, we formulate the identification of depen-dency patterns as a regularised statistical estimation problem.The resulting model can be interpreted as a set of time-varyinggraphs and provides a useful visual interpretation of networkactivity. We believe this is the first application of dynamicgraphical modelling to network traffic of this kind. Investigationsare performed on 9 days of real-world network traffic across asubset of IP’s. We demonstrate that dependency between featuresmay change across time and discuss how these change at an intraand inter-day level. Such variation in feature dependency mayhave important consequences for the design and implementationof probabilistic intrusion detection systems.
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: 1095-6859
ObjectivesComputed tomography (CT) is an essential part of preoperative planning prior to cytoreductive surgery for primary and relapsed epithelial ovarian cancer (EOC). Our aim is to correlate pre-operative CT results with intraoperative surgical and histopathological findings at debulking surgery.MethodsWe performed a systematic comparison of intraoperative tumor dissemination patterns and surgical resections with preoperative CT assessments of infiltrative disease at key resection sites, in women who underwent multivisceral debulking surgery due to EOC between January 2013 and December 2014 at a tertiary referral center. The key sites were defined as follows: diaphragmatic involvement(DI), splenic disease (SI), large (LBI) and small (SBI) bowel involvement, rectal involvement (RI), porta hepatis involvement (PHI), mesenteric disease (MI) and lymph node involvement (LNI).ResultsA total of 155 patients, mostly with FIGO stage IIIC disease (65%) were evaluated (primary = 105, relapsed = 50). Total macroscopic cytoreduction rates were: 89%. Pre-operative CT findings displayed high specificity across all tumor sites apart from the retroperitoneal lymph node status, with a specificity of 65%.The ability however of the CT to accurately identify sites affected by invasive disease was relatively low with the following sensitivities as relating to final histology:32% (DI), 26% (SI), 46% (LBI), 44% (SBI), 39% (RI), 57% (PHI), 31% (MI), 63% (LNI).ConclusionPre-operative CT imaging shows high specificity but low sensitivity in detecting tumor involvement at key sites in ovarian cancer surgery. CT findings alone should not be used for surgical decision making.
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
Seasonal variations are rarely considered a contributing component to human tissue function or health, although many diseases and physiological process display annual periodicities. Here we find more than 4,000 protein-coding mRNAs in white blood cells and adipose tissue to have seasonal expression profiles, with inverted patterns observed between Europe and Oceania. We also find the cellular composition of blood to vary by season, and these changes, which differ between the United Kingdom and The Gambia, could explain the gene expression periodicity. With regards to tissue function, the immune system has a profound pro-inflammatory transcriptomic profile during European winter, with increased levels of soluble IL-6 receptor and C-reactive protein, risk biomarkers for cardiovascular, psychiatric and autoimmune diseases that have peak incidences in winter. Circannual rhythms thus require further exploration as contributors to various aspects of human physiology and disease.
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
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