Inferring causal molecular networks: empirical assessment through a community-based effort.
Inferring causal molecular networks: empirical assessment through a community-based effort.
复制标题
推断因果分子网络：通过基于社区的努力进行的经验评估。
DOI：
10.1038/nmeth.3773
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发表时间：
2016-04
期刊：
影响因子：
48
通讯作者：
Mukherjee S
中科院分区：
文献类型：
--
作者：
Hill SM;Heiser LM;Cokelaer T;Unger M;Nesser NK;Carlin DE;Zhang Y;Sokolov A;Paull EO;Wong CK;Graim K;Bivol A;Wang H;Zhu F;Afsari B;Danilova LV;Favorov AV;Lee WS;Taylor D;Hu CW;Long BL;Noren DP;Bisberg AJ;HPN-DREAM Consortium;Mills GB;Gray JW;Kellen M;Norman T;Friend S;Qutub AA;Fertig EJ;Guan Y;Song M;Stuart JM;Spellman PT;Koeppl H;Stolovitzky G;Saez-Rodriguez J;Mukherjee S
HPN-DREAM社区挑战评估了计算方法推断因果分子网络的能力，特别关注推断癌细胞系中因果蛋白信号网络的任务。本文的在线版本（doi：10.1038/nmeth.3773）包含补充材料，可供授权用户使用。目前尚不清楚是否因果关系，而不仅仅是相关性，在分子网络中的关系可以推断在复杂的生物环境。在这里，我们描述了HPN-DREAM网络推理挑战，重点是学习信号网络中的因果影响。我们使用了来自癌细胞系的磷蛋白数据以及来自非线性动力学模型的计算机数据。使用磷蛋白数据，我们对挑战参与者提交的2,000多个网络进行了评分。这些网络跨越32个生物学背景，并根据看不见的干预数据的因果有效性进行评分。许多方法都是有效的，结合已知的生物学通常是有利的。考虑了时间过程预测和可视化的其他子挑战。我们的研究结果表明，学习因果关系可能是可行的，在复杂的设置，如疾病状态。此外，我们的评分方法提供了一种实用的方法来经验性地评估因果意义上的推断分子网络。本文的在线版本（doi：10.1038/nmeth.3773）包含补充材料，可供授权用户使用。
The HPN-DREAM community challenge assessed the ability of computational methods to infer causal molecular networks, focusing specifically on the task of inferring causal protein signaling networks in cancer cell lines. The online version of this article (doi:10.1038/nmeth.3773) contains supplementary material, which is available to authorized users. It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense. The online version of this article (doi:10.1038/nmeth.3773) contains supplementary material, which is available to authorized users.