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Genetic generalized epilepsies (GGE) are genetically determined, as their name implies and they are clinically characterized by generalized seizures involving both sides of the brain in the absence of detectable brain lesions or other known causes. GGEs are yet complex and are influenced by many different genetic and environmental factors. Methylation specific epigenetic marks are one of the players of the complex epileptogenesis scenario leading to GGE. In this study, we have set out to perform genome-wide methylation profiling to analyze GGE trios each consisting of an affected parent-offspring couple along with an unaffected parent. We have developed a novel scoring scheme within trios to categorize each locus analyzed as hypo or hypermethylated. This stringent approach classified differentially methylated genes in each trio and helped us to produce trio specific and pooled gene lists with inherited and aberrant methylation levels. In order to analyze the methylation differences from a boarder perspective, we performed enrichment analysis with these lists using the PANOGA software. This collective effort has led us to detect pathways associated with the GGE phenotype, including the neurotrophin signaling pathway. We have demonstrated a trio based approach to genome-wide DNA methylation analysis that identified individual and possibly minor changes in methylation marks that could be involved in epileptogenesis leading to GGE.

The prevalence of mobile devices in today's world caused the security of these devices questioned more frequently than ever. Android, as one of the most widely used mobile operating systems, is the most likely target for malwares through third party applications. In this work, a method has been devised to detect malwares that target Android platform, by using classification based machine learning. In this study, we use permissions of applications as the features. After the training and test steps on the dataset consisting 5271 malwares and 5097 goodwares, we conclude that Random Forest classification results in 98% performance on the classification of applications. This work emphasizes how much mobile malware classification result can be improved by a system using only the permissions data.