A feature selection technique for inference of graphs from their known topological properties: Revealing scale-free gene regulatory networks

Abstract An important problem in bioinformatics is the inference of gene regulatory networks (GRNs) from expression profiles. In general, the main limitations faced by GRN inference methods are the small number of samples with huge dimensionalities and the noisy nature of the expression measurements. Alternatives are thus needed to obtain better accuracy for the GRNs inference problem. Many pattern recognition techniques rely on prior knowledge about the problem in addition to the training data to gain statistical estimation power. This work addresses the GRN inference problem by modeling prior knowledge about the network topology. The main contribution of this paper is a novel methodology that aggregates scale-free properties to a classical low-cost feature selection method, known as Sequential Floating Forward Selection (SFFS), for guiding the inference task. Such methodology explores the search space iteratively by applying a scale-free property to reduce the search space. In this way, the search space traversed by the method integrates the exploration of all combinations of predictors set when the number of combinations is small (dimensionality 〈 k 〉 ⩽ 2 ) with a floating search when the number of combinations becomes explosive (dimensionality 〈 k 〉 ⩾ 3 ). This process is guided by scale-free prior information. Experimental results using synthetic and real data show that this technique provides smaller estimation errors than those obtained without guiding the SFFS application by the scale-free model, thus maintaining the robustness of the SFFS method. Therefore, we show that the proposed framework may be applied in combination with other existing GRN inference methods to improve the prediction accuracy of networks with scale-free properties.