Bayesian networks and set-based methods for differential study of gene regulatory networks
In the recent years, the Bayesian networks (BN) have become one of the most powerful machine learning methods to modeling graphically and probabilistically different kinds of complex systems. One of the common issues in BN structure learning is the small-data problem. In fact, the result of learning is sensible to sample size of dataset. In machine learning, the set-based learning methods such as Bootstrap ou genetic algorithms are the often used methods to dealing with the small-data problem. However, the existing methods limit generally to the fusion of a set of models, but do not allow to compare two set of models. Inspired from the obtained results of the set-based methods, we proposed a novel method based on the quasi-essential graph (QEG) and the usage of the multiple testing in order to compare two sets of BN. QEG allows to resume and visualize graphically a set of BN. The multiple testing allows to verify if the differences between two set of BN are statistically significative and to determine the position of the differences. The application on the synthetic and experimental data demonstrated the different interests of proposed method in gene regulatory networks reconstruction and perspectively in the other applications with the small dataset.