We tackle the problem of automatic detection of StructureActivity relationships by means of Inductive Logic Programming (ILP). We describe an algorithm for constructing features from background knowledge (stochastic propositionalization SP) and an abstract level representation for chemical compounds.

We consider the problem of identifying the genetic loci (called quantitative trait loci (QTLs)) contributing to variation in a quantitative trait, with data on an experimental cross. A large number of different statistical approaches to this problem have been described; most make use of multiple tests of hypotheses, and many consider models allowing only a single QTL. We feel that the problem i...

MOTIVATION In most quantitative trait locus (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection and lead to detection of spurious QTLs. To improve the robustness of QTL mapping methods, we replaced the normal distribution for residuals in multiple interacting QTL models with the normal/independent...

Quantitative genetics (QG) analyses variation in traits of humans, other animals, or plants in ways that take account of the genealogical relatedness of the individuals whose traits are observed. This article focuses on "classical" QG where the analysis of variation does not involve data on measurable genetic or environmental entities or factors. Classical QG analysis employs models of genes wi...

Since the pioneering effort of Hansch and Fujita, quantitative structure-activity relationships (QSAR) have proved valuable in optimizing lead structures. Enriching classical 3D-QSAR analysis, which exploits the three-dimensional structure of ligands, with structural information of the target has helped to improve the interpretability of the derived models and to increase their predictive power...