Analyzing genomic data using module networks

OData support
Supervisor:
Dr. Antal Péter
Department of Measurement and Information Systems

Asthma is a chronic lung disease characterized by airway inflammation, hyperresponsiveness, remodeling, and obstruction. The lung phenotype in asthma is believed to be determined by the interaction of the environment with the patient’s genetic background. This interaction leads to a dramatic change in the airway microenvironment that includes activation of inflammatory pathways, recruitment of immune cells that are not usually present in the airway, and a dramatic change in the phenotype of airway resident cells.

In the last decades the huge amount of data produced by methods resulted by the improvement of molecular biology calls for new specialty, the bioinformatics. This is a frontier of biology, statistics and informatics with the goal developing high-throughput and preferably simple algorithms, which are able to process and evaluate large datasets.

In this thesis I am presenting and evaluating a Bayes’ theorem based, intelligent method for the detection of genetic pathways, called module network. My algorithm is intended to use to investigate the genetic background of asthma.

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