Department of Computer Science, University of Texas at San Antonio, San Antonio, Texas, United States of America.
PLoS One. 2010 Feb 4;5(2):e8944. doi: 10.1371/journal.pone.0008944.
A wealth of computational methods has been developed to address problems in systems biology, such as modeling gene expression. However, to objectively evaluate and compare such methods is notoriously difficult. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) project is a community-wide effort to assess the relative strengths and weaknesses of different computational methods for a set of core problems in systems biology. This article presents a top-performing algorithm for one of the challenge problems in the third annual DREAM (DREAM3), namely the gene expression prediction challenge. In this challenge, participants are asked to predict the expression levels of a small set of genes in a yeast deletion strain, given the expression levels of all other genes in the same strain and complete gene expression data for several other yeast strains. I propose a simple -nearest-neighbor (KNN) method to solve this problem. Despite its simplicity, this method works well for this challenge, sharing the "top performer" honor with a much more sophisticated method. I also describe several alternative, simple strategies, including a modified KNN algorithm that further improves the performance of the standard KNN method. The success of these methods suggests that complex methods attempting to integrate multiple data sets do not necessarily lead to better performance than simple yet robust methods. Furthermore, none of these top-performing methods, including the one by a different team, are based on gene regulatory networks, which seems to suggest that accurately modeling gene expression using gene regulatory networks is unfortunately still a difficult task.
已经开发了大量的计算方法来解决系统生物学中的问题，例如基因表达建模。然而，客观地评估和比较这些方法是非常困难的。DREAM（反向工程评估和方法对话）项目是一个社区范围内的努力，旨在评估不同计算方法在系统生物学核心问题上的相对优势和劣势。本文介绍了第三届 DREAM（DREAM3）挑战赛中的一个核心问题的表现最佳算法，即基因表达预测挑战赛。在这个挑战中，参与者被要求根据同一菌株中所有其他基因的表达水平以及其他几个酵母菌株的完整基因表达数据，预测一小部分基因在酵母缺失菌株中的表达水平。我提出了一种简单的最近邻（KNN）方法来解决这个问题。尽管这种方法很简单，但它在这个挑战中表现良好，与一种更加复杂的方法共同获得了“表现最佳”的荣誉。我还描述了几种替代的简单策略，包括一种改进的 KNN 算法，它进一步提高了标准 KNN 方法的性能。这些方法的成功表明，试图整合多个数据集的复杂方法不一定比简单但稳健的方法表现更好。此外，包括一个不同团队提出的方法在内，这些表现最佳的方法都没有基于基因调控网络，这似乎表明，使用基因调控网络准确地建模基因表达仍然是一项困难的任务。
