Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Prediction of Enzyme Classification from Protein Sequence without the Use of Sequence Similarity
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cellular Automata: A Discrete View of the World (Wiley Series in Discrete Mathematics & Optimization)
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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A special class of n cell null boundary invertible three neighborhood CA referred to as Equal Length Cycle CA (ELCCA) is proposed in this paper to represent the features of n bit symbol strings. Necessary and sufficient conditions for generation of ELCCA has been reported. A specific set of ELCCA cycles are selected by employing the mRMR algorithm [2] popularly used for feature extraction of symbol strings. An algorithm is next developed to classify the symbol strings based on the feature set extracted. The proposed CA model has been validated for analyzing symbol string of biomolecules referred to as Enzymes. These biomolecules are classified on the basis of the catalytic reaction they participate. The symbol string classification algorithm predicts the class of any input enzyme with accuracy varying from 90.4% to 98.6%. Experimental results have been reported for 22800 enzymes with wide variation in species.