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Feature selection is an important step in gene expression data analysis. However, many feature selection methods exist and a costly experimentation is usually needed to determine the most suitable one for a given problem. This paper presents the application of gradient boosting and neural network techniques for the construction of meta-models that can recommend rankings of {feature selection - classification} algorithm pairs for new gene expression classification problems through the usage of learning-to-rank and collaborative filtering approaches. Results in a corpus of 60 public datasets show the superiority of these techniques in producing more useful rankings in relation to classical meta-models