| Περίληψη: | The advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to an enormous progress in life sciences. Among the most important innovations is the microarray technology. It allows to quantify the expression of thousands of genes simultaneously by measuring the hybridization from a tissue of interest to probes on a small glass or plastic slide. Before launching into microarray research it is important to recall that the characteristics of this data include a fair amount of noise and an atypical dimensionality (which makes difficult the use of classic statistics tools – experimental samples in the order of dozens and measured parameters in thousands or tens of thousands). Therefore, the main goal of this thesis is the development of adequate computational methods and algorithms, capable of extracting valuable biological knowledge from this type of data.
Applications of microarray technology as a tool for gene expression analysis range from the assignment of functional categories for genes of unknown biological function (based on the analysis of genes with already established biological role), to precise and early diagnosis of different tumor malignancies. However, the main goal of computational analysis of gene expression data is the extraction of regulatory knowledge at genetic level that may be used to provide a broader understanding on the functioning of complex cellular systems. In this direction, revealing the structures of regulatory networks based of gene expression data becomes a pivotal task.
The thesis contributes with a framework for the discovery of biological functional category of genes based on the synergy of ICA and a dynamic SOM-based clustering algorithm, that accurately finds groups of co-regulated genes, while identifying interesting regulatory signals within the data with the help of ICA decomposition. We also pursue the task of molecular characterization of different tumor types using gene expression profiling, by providing a novel method for tissue samples classification, based on an ensemble of classifiers sequentially trained on reweighted versions of the data. The algorithm, known as boosting, is adapted to peculiarities of gene expression data and employed in conjunction with SVMs. Additionally, the novel concept of finding predictive genes whose signatures are significant for phenotype discrimination is treated.
Finally, the thesis presents a method developed for reverse-engineering gene regulatory networks based on recurrent neuro-fuzzy networks, which exploits the advantages of fuzzy-based models, in terms of results interpretability, and those of neural systems, in terms of computational power and time series prediction capabilities.
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