Link and node prediction in metabolic networks with probabilistic logic

Authors:
Angelika Kimmig;Fabrizio Costa
Affiliations:
Departement Computerwetenschappen, K.U. Leuven, Heverlee, Belgium;Institut für Informatik, Albert-Ludwigs-Universität, Freiburg, Germany
Venue:
Bisociative Knowledge Discovery
Year:
2012

Citing 2
Cited 1

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ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
On the implementation of the probabilistic logic programming language problog

Theory and Practice of Logic Programming

Applications and evaluation: overview

Bisociative Knowledge Discovery

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Abstract

Information on metabolic processes for hundreds of organisms is available in public databases. However, this information is often incomplete or affected by uncertainty. Systems capable to perform automatic curation of these databases and capable to suggest pathway-holes fillings are therefore needed. To this end such systems should exploit data available from related organisms and cope with heterogeneous sources of information (e.g. phylogenetic relations). Here we start to investigate two fundamental problems concerning automatic metabolic networks curation, namely link prediction and node prediction using ProbLog, a simple yet powerful extension of the logic programming language Prolog with independent random variables.