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Comparative evaluation of set-level techniques in predictive classification of gene expression
(2012)
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 ...
Prediction of DNA-binding proteins from relational features
(2012)
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 ...
Gaussian Logic for Predictive Classification
(2011)
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, ...
Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search
(2012)
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 ...
Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties
(2009)
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 ...
A Restarted Strategy for Efficient Subsumption Testing
(2008)
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 ...
Fast Estimation of First Order Clause Coverage through Randomization and Maximum Likelihood
(2008)
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 ...