Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Algorithms for clustering data
Algorithms for clustering data
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Neural networks for molecular sequence classification
Mathematics and Computers in Simulation
Machine Learning - Special issue on applications in molecular biology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Weighting and Feature Selection on Gene-Expression data by the use of Genetic Algorithms
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
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In this work we present a genetic-algorithm-based approach to optimise weighted distance measurements from compositional and physical-chemical properties of biological sequences that allow a significant reduction of the computational cost associated to the distance evaluation, while maintaining a high accuracy when comparing with traditional methodologies. The strategy has a generic and parametric formulation and exhaustive tests have been performed to shown its adaptability to optimise the weights over different compositions of sequence characteristics. These fast-evaluation distances can be used to deal with large set of sequences as is nowadays imperative, and appear as an important alternative to the traditional and expensive pairwise sequence similarity criterions.