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2007
Springer

A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks

9 years 3 months ago
A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks
Biological systems consist of many components and interactions between them. In Systems Biology the principal problem is modeling complex biological systems and reconstructing interactions between their building blocks. Symbolic machine learning approaches have the power to model structured domains and relations among objects. However biological domains require uncertainty handling due to their hidden complex nature. Statistical machine learning approaches have the potential to model uncertainty in a robust manner. In this paper we apply a hybrid symbolic-statistical framework to modeling metabolic pathways and show through experiments that complex phenomenon such as biochemical reactions in cell’s metabolic networks can be modeled and simulated in the proposed framework.
Marenglen Biba, Stefano Ferilli, Nicola Di Mauro,
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where KES
Authors Marenglen Biba, Stefano Ferilli, Nicola Di Mauro, Teresa Maria Altomare Basile
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