Katerina Gkirtzou

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BACKGROUND MicroRNAs (miRNAs) are small, single stranded RNAs with a key role in post-transcriptional regulation of thousands of genes across numerous species. While several computational methods are currently available for identifying miRNA genes, accurate prediction of the mature miRNA remains a challenge. Existing approaches fall short in predicting the(More)
The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported(More)
In this paper, we explore various sparse regularization techniques for analyzing fMRI data, such as LASSO, elastic net and the recently introduced k-support norm. Employing spar-sity regularization allow us to handle the curse of dimension-ality, a problem commonly found in fMRI analysis. We test these methods on real data of both healthy subjects as well(More)
We explore various sparse regularization techniques for analyzing fMRI data, such as the ℓ1 norm (often called LASSO in the context of a squared loss function), elastic net, and the recently introduced k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we(More)
The development of the Semantic Web proceeds in steps, building each layer on top of the other. Currently, the focus of research efforts is concentrated on logic and proofs, both of which are essential, since they will allow systems to infer new knowledge by applying principles on the existing data and explain their actions. Research is shifting towards the(More)
fMRI analysis has most often been approached with linear methods. However, this disregards information encoded in the relationships between voxels. We propose to exploit the inherent spatial structure of the brain to improve the prediction performance of fMRI analysis. We do so in an exploratory fashion by representing the fMRI data by graphs. We use the(More)