Reverse-engineering algorithms benchmarks

So, which one of the many dozens of available reverse-engineering algorithms should you use? Well, the answer depends on which data you have: different algorithms are good in different contexts. Some researchers have expended a considerable effort to answer this question, and I list some of these articles below.

Bibliography

1. M Bansal, V Belcastro, A Ambesi-Impiombato and D di Bernardo. How to infer gene networks from expression profiles. Molecular Systems Biology 3:78, 2007. PDF.

   Abstract

   Inferring, or reverse-engineering, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverseengineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

   Comments

   A very nicely written study of comparative performance of some of available reverse-engineering algorithms on in silico and wet data.