Towards a rigorous assessment of systems biology models: the DREAM3 challenges.
Towards a rigorous assessment of systems biology models: the DREAM3 challenges.
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DOI：
10.1371/journal.pone.0009202
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发表时间：
2010-02-23
期刊：
影响因子：
3.7
通讯作者：
Stolovitzky G
中科院分区：
文献类型：
--
作者：
Prill RJ;Marbach D;Saez-Rodriguez J;Sorger PK;Alexopoulos LG;Xue X;Clarke ND;Altan-Bonnet G;Stolovitzky G
系统生物学已经接受了计算建模，以应对当代数据集的数量性质和日益扩大的规模。随着分子图谱技术的发展，数据的冲击正在加速。逆向工程评估和方法对话(DREAM)是一项社区努力，旨在通过年度逆向工程挑战来促进关于系统生物学模型的设计、应用和评估的讨论。我们描述了我们对与第三届DREAM3大会相关的四个挑战的评估，这些挑战后来被称为DREAM3挑战：信号级联识别、信号响应预测、基因表达预测和DREAM3在计算机网络中的挑战。这些挑战基于匿名数据集，测试了参与者在网络推理和测量预测方面的能力。40个团队提交了413个预测网络和测量测试集。总体而言，确定了少数表现最好的团队，而大多数团队做出的预测相当于随机。与直觉相反的是，在某些情况下，组合多个团队(包括实力较弱的团队)的预测可以提高预测能力，而不是任何单一方法。Dream为系统生物学建模的实践者提供了宝贵的反馈。从社区预测中吸取的经验教训为解释科学文献中描述的算法有效性的主张提供了亟需的背景。
Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.
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DOI：
10.1196/annals.1407.006
发表时间：
2007-01-01
期刊：
REVERSE ENGINEERING BIOLOGICAL NETWORKS
影响因子：
--
作者：
Camacho, Diogo;Licona, Paola Vera;Laubenbacher, Reinhard
通讯作者：
Laubenbacher, Reinhard
DOI：
10.1098/rstb.2005.1810
发表时间：
2006-03-29
影响因子：
6.3
作者：
Moult, J
通讯作者：
Moult, J
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP
影响因子：
12.3
作者：
Granek, JA;Clarke, ND
通讯作者：
Clarke, ND
影响因子：
5.8
作者：
Saez-Rodriguez, Julio;Goldsipe, Arthur;Sorger, Peter K.
通讯作者：
Sorger, Peter K.