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Common evolutionary approaches to protein-ligand docking optimization use mutation operators based on Gaussian and Cauchy distributions, with local search hybrids. The choice of a local search method is important for an efficient algorithm. We investigate the impact of local search with mutation operators by performing a locality analysis. High locality means that small variations in the genotype...
The performance of artificial neural networks is largely influenced by the value of their parameters. Among these free parameters, one can mention those related with the network architecture, e.g., number of hidden neurons, number of hidden layers, activation function, and those associated with a learning algorithm, e.g., learning rate. Optimization techniques, often genetic algorithms, have been...
Machine Learning techniques have been largely applied to the problem of class prediction in microarray data. Nevertheless, current approaches to select appropriate methods for such task often result unsatisfactory in many ways, instigating the need for the development of tools to automate the process. In this context, the authors introduce the use of metalearning in the specific domain of gene expression...
Haplotype inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This piece of information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as...
One of the main kinds of computational tasks regarding gene expression data is the construction of classifiers (models), often via some machine learning (ML) technique and given data sets, to automatically discriminate expression patterns from cancer (tumor) and normal tissues or from subtypes of cancers. A very distinctive characteristic of these data sets is its high dimensionality and the fewer...
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