Multi-Objective Parameter Selection for Classifiers

Setting the free parameters of classifiers to different values can have a profound impact on their performance. For some methods, specialized tuning algorithms have been developed. These approaches mostly tune parameters according to a single criterion, such as the cross-validation error. However, it is sometimes desirable to obtain parameter values that optimize several concurrent - often conflicting - criteria. The TunePareto package provides a general and highly customizable framework to select optimal parameters for classifiers according to multiple objectives. Several strategies for sampling and optimizing parameters are supplied. The algorithm determines a set of Pareto-optimal parameter configurations and leaves the ultimate decision on the weighting of objectives to the researcher. Decision support is provided by novel visualization techniques

Cooperative development of logical modelling standards and tools with CoLoMoTo

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments. Contact: [email protected]

Multi-objective selection for collecting cluster alternatives

Cluster analysis, Multi-objective optimization, Cluster number estimation, Cluster validation,

Boolean networks for modeling and analysis of gene regulation

Gene-regulatory networks control the expression of genes and therefore the phenotype of cells. Modeling and simulation of such networks can provide deep insights into the functioning of cells. Boolean networks are a commonly used technique to model gene-regulatory networks. We introduce methods to construct Boolean networks from literature knowledge and to analyze their dynamics. In particular, methods to identify and analyze attractors are presented. In simulations on three biological networks, we analyze the robustness of attractors. These evaluations confirm the biological relevance of previously identified attractors