Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation
The Journal of Machine Learning Research
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Transcription control networks have a scale-free topological structure: While most genes are involved in a reduced number of links, a few hubs or key regulators are connected to a significantly large number of nodes. Several methods have been developed for the reconstruction of these networks from gene expression data, e.g. ARACNE. However, few of them take into account the scale-free structure of transcription networks. In this paper, we focus on the hubs that commonly appear in scale-free networks. First, three feature selection methods are proposed for the identification of those genes that are likely to be hubs and second, we introduce an improvement in ARACNE so that this technique can take into account the list of hub genes generated by the feature selection methods. Experiments with synthetic gene expression data validate the accuracy of the feature selection methods in the task of identifying hub genes. When ARACNE is combined with the output of these methods, we achieve up to a 62% improvement in performance over the original reconstruction algorithm. Finally, the best method for identifying hub genes is validated on a set of expression profiles from yeast.