Inference algorithms for gene networks: a statistical mechanics analysis

The inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators

Classification and sparse-signature extraction from gene-expression data

In this work we suggest a statistical mechanics approach to the classification of high-dimensional data according to a binary label. We propose an algorithm whose aim is twofold: First it learns a classifier from a relatively small number of data, second it extracts a sparse signature, {\it i.e.} a lower-dimensional subspace carrying the information needed for the classification. In particular the second part of the task is NP-hard, therefore we propose a statistical-mechanics based message-passing approach. The resulting algorithm is firstly tested on artificial data to prove its validity, but also to elucidate possible limitations. As an important application, we consider the classification of gene-expression data measured in various types of cancer tissues. We find that, despite the currently low quantity and quality of available data (the number of available samples is much smaller than the number of measured genes, limiting thus strongly the predictive capacities), the algorithm performs slightly better than many state-of-the-art approaches in bioinformatics.Comment: 15 pages, 13 eps figure