Abstract

Inferring gene networks is a daunting task. We here describe several algorithms we used in the Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Reverse Engineering Competition 2007: an algorithm based on first-order partial correlation for discovering BCL6 targets in Challenge 1 and an algorithm using nonlinear optimization with winning performance in Challenge 3. After the gold standards for the challenges were released, the performance of alternative variants of the algorithms could be evaluated. The DREAM competition taught us some strong lessons. Amazingly, simpler methods performed in general better than more advanced, theoretically motivated approaches. Also, the challenges strongly showed that inferring gene networks requires controlled experimentation using a well-defined experimental design. Analyzing data obtained through merging many unrelated datasets indeed resulted in weak performances of all algorithms, while algorithms that explicitly took the experimental design into account performed best.

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