Baralla Angela, Mentzen Wieslawa I, de la Fuente Alberto
Dipartimento di Scienze Biomediche, Laboratorio di ricerca e diagnosi di proteomica, metabolomica e biologia molecolare clinica, Università degli Studi di Sassari, Sassari, Italy.
Ann N Y Acad Sci. 2009 Mar;1158:246-56. doi: 10.1111/j.1749-6632.2008.04099.x.
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.
推断基因网络是一项艰巨的任务。我们在此描述了我们在2007年逆向工程评估与方法对话（DREAM2）逆向工程竞赛中使用的几种算法：一种基于一阶偏相关用于在挑战1中发现BCL6靶点的算法，以及一种在挑战3中具有优异性能的使用非线性优化的算法。在挑战的金标准发布后，可以评估算法替代变体的性能。DREAM竞赛给了我们一些深刻的教训。令人惊讶的是，一般来说，更简单的方法比更先进的、理论上有动机的方法表现更好。此外，这些挑战有力地表明，推断基因网络需要使用明确界定的实验设计进行受控实验。分析通过合并许多不相关数据集获得的数据确实导致所有算法的性能不佳，而明确考虑实验设计的算法表现最佳。
