Selected Publications

Advances in sequencing techniques have massively increased the publicly accessible -omics data and thus enable further and more extensive research opportunities on genome diversity. The concept of the pangenome refers to the union of gene families shared by a set of genomes. Several studies have implemented specific pangenome analyses for a variety of organisms, ranging from microbes to viruses and plants , leading to genomic projects of various scales and the advancement of general understanding of evolutionary mechanisms, generating usable knowledge across multiple sectors.
In ISMB/ECCB 2017, 2017.

The research on epileptiform discharges (ED) in electroencephalographic recordings of patients with epilepsy has shown that changes are observed in connectivity of the brain area correlation networks. The data processed describe various epileptiform discharge situations. In some cases, the technique of transcranial magnetic stimulation (TMS), a noninvasive stimulation technique applicable in the field of focal epilepsy treatment, is used. In the current study, we examine the connectivity change of networks, formed by analyzing the correlation of selected EEG channels using cross correlation and cross mutual information networks.
In IPSC, 2013.

Recent Publications

Computing Pangenome statistics in R

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Enabling Active and Healthy Ageing (AHA) Decision Support Systems with the smart collection of TV usage patterns.

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Towards a Quantified-Self web application for seniors' self-tracking.

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Comparison of correlation measures for detection of brain network connectivity alterations during epileptiform discharges

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Recent Posts

An idea that I have been discussing with friend and former co-student Alexandros Sousos finally hatches to reality: A first meetup will happen at the 12th of December in order to discuss and essentially materialize an R user community in Thessaloniki, Greece! It will take place in Lambda Space a super friendly hackerspace! More details can be found in the event announcement !

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This post is the first of a series of posts on model parameter tuning: We will see code to tune Random Forest models with a technique called grid search. Random Forests is a popular ensemble learning method, introduced in its “current” form by Leo Breiman in a same-titled paper. Random forests can be used for classification or regression and offer “protection” from the overfitting that is sometimes observed in single decision trees.

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Projects