Background: Analysis of gene expression data in terms of a priori-defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by ...

Background: The process of protein-DNA binding has an essential role in the biological processing of genetic information. We use relational machine learning to predict DNA-binding propensity of proteins from their ...

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, ...

We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-ofthe- art methods based on constructing an ad-hoc set of features describing physicochemical properties of ...

We describe an algorithm for constructing a set of acyclic conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our ...

We study runtime distributions of subsumption testing. On graph data randomly sampled from two different generative models we observe a gradual growth of the tails of the distributions as a function of the problem instance ...

In inductive logic programming, µ-subsumption is a widely used coveragetest. Unfortunately, testing µ-subsumption is NP-complete, which represents a crucial efficiency bottleneck for many relational learners. In this ...