Gergely Hajós

Budapest University of Technology and Economics, Budapest, Budapest fovaros, Hungary

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Publications (4)3.53 Total impact

  • Probabilistic graphical models in genetics, genomics, postgenomics, Edited by Christine Sinoquet, Raphael Mourad, 01/2014: chapter Bayesian, systems-based, multilevel analysis of associations for complex phenotypes: from interpretation to decisions; Oxford University Press.
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    ABSTRACT: Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called bayesian network based bayesian multilevel analysis of relevance (BN-BMLA). This method uses bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated.With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2-1.8); p = 3×10(-4)). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics.In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance.
    PLoS ONE 03/2012; 7(3):e33573. · 3.53 Impact Factor
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    ABSTRACT: Psychoneuroimmunologic studies on positive emotions are few, and their clinical relevance is limited. This "SHoRT" (Smiling Hospital Research Team) study evaluates the effects that Smiling Hospital artists have on hospitalized children. Blood samples were taken in a non-painful way through branules in an accredited Infectology Ward, 30 minutes before and 1 hour after a visit of tale tellers, puppeteers and handicraft artists. 24 children were visited and 9 were included in the control group. Blood lymphocyte counts and Th1/Th2 cytokine levels were determined. Artists evaluated their effect on a subjective scale. In the visited group, the increase of lymphocytes was 8.43% higher, the decrease was 12.45% lower, and the proportion of children showing increased lymphocyte counts was more increased. Changes were more marked after more successful visits. Authors found non-significant, still considerable changes in interferon-γ level (p < 0.055) and in Th1/Th2 cytokine ratios. This pediatric study suggests that immunological changes may develop when more attention is given to hospitalized children.
    Orvosi Hetilap 10/2011; 152(43):1739-44.
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    ABSTRACT: The accumulation of electronically accessible data and knowledge are posing theoretical and practical challenges for study design and statistical data analysis. It consists of the use of the results of earlier high-throughput measurements of genetic variations, microRNA, and gene expression levels, and the use of the biological knowledge bases. We investigate fusion in the phases of study design, data analysis, and interpretation; specifically, we present methodologies and bioinformatic tools in the Bayesian framework to deepen, lengthen, and broaden this fusion. First, we overview a Bayesian decision support for design of partial genetic association studies (GASs) incorporating domain literature, knowledge bases, and results of analysis of earlier studies. Second, we present a Bayesian multilevel analysis (BMLA) for GAS, which performs an integrated analysis at the univariate and multivariate levels, and at the level of interactions. Third, we present a Bayesian logic to support interpretation, which integrates the results of data analysis and factual domain knowledge. Finally, we discuss the advantages of the Bayesian framework to cope with small sample size, fusion of data and knowledge, challenges of multiple testing, meta-analysis, and positive results bias (i.e., the communication of scientific uncertainty). The genomics of asthma will serve as an application domain.
    10/2009: pages 157-185;