Grzegorz Zycinski

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High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of(More)
In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g.(More)
In computational biology, the analysis of high-throughput data poses several issues on the reliability, reproducibility and interpretability of the results. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the(More)
The great amount of information produced in the field of molecular biology and genetics opened enormous possibilities for life science researchers to analyze and understand biological phenomenons. This wealth, however, brings new problems, such as how to integrate existing information sources, and how to use already well-developed statistical techniques in(More)
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