Integrating Prior Knowledge into the Fitness Function of an Evolutionary Algorithm for Deriving Gene Regulatory Networks
The topic of gene regulation is a major research area in the bioinformatics community. In this thesis prior knowledge from Gene Ontology in the form of templates is integrated into the fitness function of an evolutionary algorithm to predict gene regulatory networks. The resulting multi-objective fitness functions are then tested with MAPK network data taken from KEGG to evaluate their respective performances. The results are presented and analyzed. However, a clear tendency cannot be observed. The results are nevertheless promising and can provide motivation for further research in that direction. Therefore different ideas and approaches are suggested for future work.
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