Haydar Koç

Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria

ABSTRACT Modeling of count responses is widely performed via Poisson regression models. This paper covers the problem of variable selection in Poisson regression analysis. The basic emphasis of this paper is to present the usefulness of information complexity-based criteria for Poisson regression. Particle swarm optimization (PSO) algorithm was adopted to minimize the information criteria. A real dataset example and two simulation studies were conducted for highly collinear and lowly correlated datasets. Results demonstrate the capability of information complexity-type criteria. According to the results, information complexity-type criteria can be effectively used instead of classical criteria in count data modeling via the PSO algorithm.

Lack of association between MAOA-uVNTR variants and excessive daytime sleepiness

Sleep disorders are highly prevalent in the population and have dramatic health, social, and economic impacts. However, their treatments may remain symptomatic due to ignorance of molecular factors which may provide fundamental insights into the neurological bases of sleep. Excessive daytime sleepiness (EDS) is a common complaint encountered in neurological practice with significant effects both on individuals and on society. We aimed to investigate the role of monoamine oxidase A (MAOA) as a candidate gene in EDS. Epworth sleepiness scale (ESS) was applied to 221 subjects who were also genotyped for MAOA upstream variable number of tandem repeats (MAOA-uVNTR). Patient group displayed higher ESS values (mean 12.67) when compared with the control group (mean 6.38). However, MAOA-uVNTR genotypes did not show a significant association with ESS scores neither on women nor on men. Finally, these data suggest further replications in different populations. Moreover, the investigation of some other genes together with MAOA and/or some possible regulatory molecular mechanisms may offer a more comprehensive approach in the role of genetic factors contributing to EDS.