HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Machine Learning Bio-molecular Interactions from Temporal Logic Properties

Abstract : With the advent of formal languages for modeling bio-molecu\-lar interaction systems, the design of automated reasoning tools to assist the biologist becomes possible. The biochemical abstract machine BIOCHAM software environment offers a rule-based language to model bio-molecular interactions and an original temporal logic based language to formalize the biological properties of the system. Building on these two formal languages, machine learning techniques can be used to infer new molecular interaction rules from temporal properties. In this context, the aim is to semi-automatically correct or complete models from observed biological properties of the system. Machine learning from temporal logic formulae is quite new however, both from the machine learning perspective and from the Systems Biology perspective. In this paper we present an ad-hoc enumerative method for structural learning from temporal properties and report on the evaluation of this method on formal biological models of the literature.
Complete list of metadata

Contributor : Sylvain Soliman Connect in order to contact the contributor
Submitted on : Friday, June 17, 2005 - 12:02:45 PM
Last modification on : Thursday, February 3, 2022 - 11:18:19 AM
Long-term archiving on: : Thursday, April 1, 2010 - 9:42:58 PM


  • HAL Id : inria-00000117, version 1



Laurence Calzone, Nathalie Chabrier-Rivier, Francois Fages, Lucie Gentils, Sylvain Soliman. Machine Learning Bio-molecular Interactions from Temporal Logic Properties. Third Workshop on Computational Methods in Systems Biology, Apr 2005, Edinburgh, Scotland. ⟨inria-00000117⟩



Record views


Files downloads