Abstract
This paper describes a novel meta-learning (MTL) based methodology used to optimize a neural network based inference system. The inference system being optimized is part of a bioinformatic application built to implement a systematic search scheme for the identification of genes which encode enzymes of metabolic pathways. Different MTL implementations are contrasted with manually optimized inference systems. The MTL based approach was found to be flexible and able to produce better results than manual optimization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
NCBI: Basic Local Alignment Search Tool, http://blast.ncbi.nlm.nih.gov/Blast.cgi
Invitrogen Corporation: Vector NTI Software, http://www.invitrogen.com/site/us/en/home/Applications/Cloning/Vector-Design-Software.html
National Institute of General Medical Sciences: MetaCyc Encyclopedia of Metabolic Pathways, http://www.metacyc.org/
University of Minnesota: University of Minnesota Biocatalysis/Biodegradation Database, http://umbbd.msi.umn.edu/
Kanehisa Laboratories: KEGG Pathways Database, http://www.genome.ad.jp/kegg/pathway.html
NCBI: National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov/
Arredondo, T., Seeger, M., Dombrovskaia, L., Avarias, J., Calderón, F., Candel, D., Muñoz, F., Latorre, V., Agulló, L., Cordova, M., Gomez, L.: Bioinformatics integration framework for metabolic pathway data-mining. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 917–926. Springer, Heidelberg (2006)
GeXpert Open Source Project, Available in Sourceforge http://sourceforge.net/projects/gexpert
Sun, J., Zeng, A.P.: IdentiCS - Identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence. BMC Bioinformatics 5, 112 (2004)
Phylogist UTFSM Bioinformatics Project Website, http://www.feriadesoftware.cl/2007/phylogist/producto.html
Arredondo, T., Vásquez, F., Candel, D., Dombrovskaia, L., Agulló, L., Córdova, M., Latorre-Reyes, V., Calderón, F., Seeger, M.: Dynamic Penalty Based GA for Inducing Fuzzy Inference Systems. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 957–966. Springer, Heidelberg (2007)
Leiva, M., Arredondo, T., Candel, D., Dombrovskaia, L., Agulló, L., Seeger, M., Vásquez, F.: Feed-Forward Artificial Neural Network Based Inference System Applied in Bioinformatics Data-Mining. In: Conference Paper IJCNN 2009, Atlanta, USA, June 14-19, pp. 1744–1749 (2009)
Burkholderia xenovorans LB400 at the Joint Genome Institute, http://genome.jgi-psf.org/finished_microbes/burfu/burfu.home.html
Pinney, J., Shirley, M.W., McConkey, G.A., Westhead, D.R.: metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Research 33(4), 1399–1409 (2005)
Notebaart, R., van Enckevort, F., Francke, C., Siezen, R., Teusink, B.: Accelerating the reconstruction of genome-scale metabolic networks. BMC Bioinformatics 7, 296 (2006)
Anderson, M.L., Oates, T.: A review of recent research in metareasoning and metalearning. AI Magazine (2007)
Vilalta, R., Drissi, Y.: A Perspective View and Survey of Meta-learning. Journal of Artificial Intelligence Review 18(2), 77–95 (2002)
Bensusan, H., Giraud-Carrier, C.: Casa Batlo in Passeig or landmarking the expertise space. In: Eleventh European Conference on Machine Learning, Workshop on Meta-learning: Building Automatic Advice Strategies for Model Selection and Method Combination, Barcelona, Spain (2000)
Wolpert, D.: Stacked Generalization. Neural Networks 5, 241–259 (1992)
Aran, O., Yildiz, T., Alpaydin, E.: An incremental framework based cross-validation for estimating the architecture of a multilayer perceptron. International Journal of Pattern Recognition and Artificial Intelligence 23(2), 159–190 (2009)
Hong, T.P., Lin, W.T., Chen, C.H., Ouyang, C.S.: Learning Membership Functions in Takagi-Sugeno Fuzzy Systems by Genetic Algorithms. In: First Asian Conference on Intelligent Information and Database Systems, pp. 301–306 (2009)
Pedersen, M.E.H., Chipperfield, A.J.: Simplifying Particle Swarm Optimization. Applied Soft Computing (2009)
Abraham, A.: Meta-learning Evolutionary Artificial Neural Networks. Neurocomputing 56 (January 2004)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Watkins, D.: Clementine’s Neural Networks Technical Overview. Technical Report (1997)
Bouckaert, R.R., Frank, E.: Evaluating the replicability of significance tests for comparing learning algorithms. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 3–12. Springer, Heidelberg (2004)
De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Dissertation. University of Michigan, Ann Arbor, MI, USA. AAI7609381 (1975)
Grefenstette, J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics in Systems, Man and Cybernetics 16(1) (1986)
Juels, A., Wattenberg, M.: Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms, Technical Report CSD-94-834, Department of Computer Science, University of California at Berkeley (1994)
SimMetaLib: Simple Meta-Learning Library, http://profesores.elo.utfsm.cl/~tarredondo/simmetalib.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arredondo, T.V., Ormazábal, W.O., Candel, D.C., Creixell, W. (2011). Meta-learning Based Optimization of Metabolic Pathway Data-Mining Inference System. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21827-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-21827-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21826-2
Online ISBN: 978-3-642-21827-9
eBook Packages: Computer ScienceComputer Science (R0)