"Inferring Regulatory Interactions from Microarray Data Using the FABIA Biclustering Method: A Case Study for E.coli K-12"
Inferring Regulatory Interactions from Microarray Data Using the FABIA Biclustering Method: A Case Study for E.coli K-12
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In modern bioinformatics there is a broad spectrum of applications for gene expression levels of microarray experiments. This thesis discusses the applicability of microarray experiments to infer regulatory interactions, or in other words, how microarray experiments can be used to identify genes regulated by a common transcription
While there are methods using pairwise comparison of gene expression values to determine which genes are regulated by a known transcription factor, the applicability of biclustering methods is barely explored. For this reason this thesis puts huge emphasis on the application of the FABIA biclustering method to infer regulatory interactions.
Potential criteria for choosing optimal parameters of the FABIA method are discussed and biclustering results are compared with known transcription factors of RegulonDB. For this a method called Rainbow Relation Diagram is introduced.
In addition results of the FABIA biclustering are compared with results of a pairwise method called CLR. An attempt is being made to answer the question: "How can a pairwise method be compared with a biclustering method?"