Determining the Significance of Input Parameters using Sensitivity Analysis
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Dynamic penalty based GA for inducing fuzzy inference systems
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Bioinformatics integration framework for metabolic pathway data-mining
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Meta-learning based optimization of metabolic pathway data-mining inference system
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Inference system using softcomputing and mixed data applied in metabolic pathway datamining
International Journal of Data Mining and Bioinformatics
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This paper describes a neural network based inference system developed as part of a bioinformatic application in order to help implement a systematic search scheme for the identification of genes which encode enzymes of metabolic pathways. The inference system uses BLAST sequence alignment values as inputs and generates a classification of the best candidates for inclusion in a metabolic pathway map. The system considers a workflow that allows the user to provide feedback with their final classification decisions. These are stored in conjunction with analyzed sequences for re-training and constant inference system improvement. The construction of the inference system involved the study of various neural topologies and training data models. Of the many training data models analyzed three are currently presented for comparison: using the BLAST algorithm's parameters directly, using standardized parameters determined by human experts, and a new proposal for input parameter normalization. The neural network was tested with all three data models. The three models enabled the inference system to perform a satisfactory rating of the candidates. Our proposal for parameter normalization produced the best model with several data sets showing a highly accurate prediction capability.