Abstract
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.
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Biba, M., Ferilli, S., Di Mauro, N., Basile, T.M.A. (2007). A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_17
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DOI: https://doi.org/10.1007/978-3-540-74819-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74817-5
Online ISBN: 978-3-540-74819-9
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