Statistical modelling of functional data from biological systems
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The collection of high-resolution temporal data from complex molecular biological systems has become of great importance over the past decades. With an increased quality and quantity of available temporal data, new research possibilities are created. Corresponding statistical or mathematical methods often perform, however, unsatisfactorily by not considering the full information. As a result, the applied methods are either not capable or unavailable of handling new research challenges. In the present book, we consider the analysis of data from biological systems where knowledge about a certain...
The collection of high-resolution temporal data from complex molecular biological systems has become of great importance over the past decades. With an increased quality and quantity of available temporal data, new research possibilities are created. Corresponding statistical or mathematical methods often perform, however, unsatisfactorily by not considering the full information. As a result, the applied methods are either not capable or unavailable of handling new research challenges. In the present book, we consider the analysis of data from biological systems where knowledge about a certain system is available and yet some indeterminacies about the system remain. Because the system cannot be fully described our approach relies on modelling of the underdetermined parts with novel techniques. Therefore, we propose novel methods which are either able to outperform existing ones or their application suggests additional aspects on the regulation and composition of the studied biological systems. The methods are evaluated on a large number of simulated data scenarios. Moreover, we investigate real-world data examples where results suggest novel insights into the studied applications.
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