Abstract
In this paper we present a methodology that can be used to design experiments of complex systems characterized by a huge number of variables. The strategy combines the evolutionary principles with the information provided by statistical models tailored to the problem under consideration. Here, we are concerned with the process of design molecules, which is a quite challenging problem due to the presence of a high number of variables with a binary structure. Recent works on clustering of binary data and variable selection in the high-dimensional setting allow to develop an approach capable of recovering useful information derived from the incorporation of a grouping structure into the model.
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Acknowledgements
The authors would like to acknowledge Professor Philip J. Brown and the GlaxoSmithKline Medicines Research Centre (UK) for the very fruitful collaboration in developing this research.
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Mameli, V., Lunardon, N., Khoroshiltseva, M., Slanzi, D., Poli, I. (2017). Reducing Dimensionality in Molecular Systems: A Bayesian Non-parametric Approach. In: Rossi, F., Piotto, S., Concilio, S. (eds) Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. WIVACE 2016. Communications in Computer and Information Science, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-57711-1_10
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