Abstract
The cell is an entity composed of several thousand types of interacting proteins. Our goal is to comprehend the biological system using only the revelent information which means that we will be able to reduce or to indicate the main metabolites necessary to measure. In this paper, it is shown how the Artificial Intelligence description method functioning on the basis of Inductive Logic Programming can be used successfully to describe essential aspects of cellular regulation. The results obtained shows that the ILP tool CF-induction discovers the activities of enzymes on glycolyse metabolic pathway when only partial information about it has been used. This procedure is based on the filtering of the high processes to reduce the space search.
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References
Breiman, L.: Bagging predictors. Machine Learning 26, 123–140 (1996)
Chassagnole, C., Rodrigues, J.C., Doncescu, A., Yang, L.T.: Differential evolutionary algorithms for in vivo dynamic analysis of glycolysis and pentose phosphate pathway in Escherichia Coli. In: Zomaya, A. (ed.) Parallel Computing in Bioinformatics and Computational Biology, Jossey-Bass an imprint of Wiley Book. ISBN 0-471-71848, -3
Doncescu, A., Yamamoto, Y., Inoue, K.: Biological Systems Analysis using Inductive Logic Programming. In: The 2007 IEEE International Symposium on Bioinformatics and Life Science Computing BLSC07, Niagara Fall, Ontario, Canada (2007)
Inoue, K.: Linear resolution for consequence finding. Artificial Intelligence 56, 301–353 (1992)
Inoue, K.: Induction as consequence finding. Machine Learning 55, 109–135 (2004)
Juvan, P., Demsar, J., Shaulsky, G., Zupan, B.: GenePath: from mutation to genetic networks and back. Nucleic Acids Research 33, 749–752 (2005)
King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., Kell, D., Oliver, S.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)
Muggleton, S.: Inverse entailment and Progol. New Gen. Comput. 13, 245–862 (1995)
Muggleton, S., Firth, J.: CProgol4.4: a tutorial introduction. Report of Department of Computer Science, University of York
Nabeshima, H., Iwanuma, K., Inoue, K.: SOLAR: a consequence finding system for advanced reasoning. In: Mayer, M.C., Pirri, F. (eds.) TABLEAUX 2003. LNCS, vol. 2796, pp. 257–263. Springer, Heidelberg (2003)
Schugerl, K., Bellgardt, K.H. (eds.): Bioreaction Engineering: Modeling and Control. Springer, Heidelberg (2000)
Stephanopoulos, G., Aristidou, A., Nielsen, J.: Metabolic engineering. Academic Press, London (1998)
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S.: Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64, 209–230 (2006)
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Doncescu, A., Inoue, K., Yamamoto, Y. (2007). Knowledge Based Discovery in Systems Biology Using CF-Induction. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_39
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DOI: https://doi.org/10.1007/978-3-540-73325-6_39
Publisher Name: Springer, Berlin, Heidelberg
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