[Show abstract][Hide abstract] ABSTRACT: The Bioinformatics book covers new topics in the rapidly expanding field of bioinformatics, from next-generation sequencing to drug discovery and metagenomics.
The first two chapters overviews genetic measurement methods. The next four chapters discuss topics related to the effect of genetic variants from protein modeling to gene regulatory networks. Standard statistical analysis in association studies are discussed in the next two chapters. The systems biology approach is illustrated by discussing a systems-based biomarker analysis method, the graph-based network science, the dynamical systems based approaches and a Bayesian causal inference method in subsequent chapters. The next chapter discusses text-mining methods in biomedicine, especially their application in interpretation and translation. The decision theoretic approach to study design, especially multi-stage, sequential study design is discussed in the next chapter, introducing the concepts of value of information and the expected value of an experiment. Next, the heterogeneity of biomedical big data sources is overviewed, together with data and knowledge fusion methods, and with the discussion of semantic publishing, which can lead to a new unification of biomedicine. Subsequently, bioinformatic workflow methods are summarized. At last, drug discovery methods are overviewed with an outlook for personalized medicine and the final chapter presents the main steps and workflows in metagenomics.
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.
[Show abstract][Hide abstract] 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. DOI:10.1371/journal.pone.0033573 · 3.23 Impact Factor
[Show abstract][Hide abstract] 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. DOI:10.1556/OH.2011.29228
[Show abstract][Hide abstract] 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