Algorithms for acyclic database schemes
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
Fast Counting with Bounded Treewidth
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Integrating Multiple-Platform Expression Data through Gene Set Features
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Group lasso with overlap and graph lasso
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Probabilistic inductive logic programming
Subgroup discovery using bump hunting on multi-relational histograms
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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We describe a statistical relational learning framework called Gaussian Logic capable to work efficiently with combinations of relational and numerical data. The framework assumes that, for a fixed relational structure, the numerical data can be modelled by a multivariate normal distribution. We demonstrate how the Gaussian Logic framework can be applied to predictive classification problems. In experiments, we first show an application of the framework for the prediction of DNAbinding propensity of proteins. Next, we show how the Gaussian Logic framework can be used to find motifs describing highly correlated gene groups in gene-expression data which are then used in a set-level-based classification method.