Piet van Remortel

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The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there(More)
In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the(More)
A major obstacle which limits the possibilities of a number of machine learning techniques like the genetic algorithm is the lack of mechanisms which allow for the dynamic construction of hierarchically organized solutions or artifacts. In the context of the GA, such mechanisms would require some form grouping or social behavior. Yet, this type of behavior(More)
Evolutionary Algorithms (EAs) are in this case counter-intuitive since they try to evolve a solution for the entire problem as a whole. EAs may show improvement when they can create more complex evolutionary units through some form of cooperative combination of sub-solutions similar to divide-and-conquer. In other words, instead of trying to evolve a single(More)