Extensive evaluation of the generalized relevance network approach to inferring gene regulatory networks.

Background: The generalized relevance network approach to network inference reconstructs network links based on the strength of associations between data in individual network nodes. It can reconstruct undirected networks, i.e.

Modeling the risk of water pollution by pesticides from imbalanced data.

The pollution of ground and surface waters with pesticides is a serious ecological issue that requires adequate treatment. Most of the existing water pollution models are mechanistic mathematical models. While they have made a significant contribution to understanding the transfer processes, they face the problem of validation because of their complexity, the user subjectivity in their parameterization, and the lack of empirical data for validation.

Semi-Supervised Multi-View Learning for Gene Network Reconstruction.

The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets.

Modeling water outflow from tile-drained agricultural fields.

The estimation of the pollution risk of surface and ground water with plant protection products applied on fields depends highly on the reliable prediction of the water outflows over (surface runoff) and through (discharge through sub-surface drainage systems) the soil. In previous studies, water movement through the soil has been simulated mainly using physically-based models. The most frequently used models for predicting soil water movement are MACRO, HYDRUS-1D/2D and Root Zone Water Quality Model.