Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data.
Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data.
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DOI：
10.1186/1471-2105-14-340
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发表时间：
2013-11-23
影响因子：
3
通讯作者：
Pratt D
中科院分区：
文献类型：
--
作者：
Catlett NL;Bargnesi AJ;Ungerer S;Seagaran T;Ladd W;Elliston KO;Pratt D
基因表达谱分析和其他基因组规模的测量技术提供了有关化学或遗传干扰或疾病状态导致的分子变化的全面信息。一个关键的挑战是开发方法来解释这些大规模的数据集，以确定特定的生物机制，可以提供实验验证的假设，并导致疾病和药物作用的理解。我们提出了一个详细的描述反向因果推理（RCR），反向工程方法来推断分子分析数据的机制假说。这种方法需要以小网络的形式存在的先验知识，这些小网络将代表生物机制的关键上游控制器节点与下游可测量的量因果地联系起来。这些小的有向网络是从文献策划的定性生物因果关系的知识库中生成的，这些关系表示为网络。小机制网络作为假设来解释观测到的差分测量。我们提供了一个简单的实现这种方法，哨子，专门面向基因表达数据的分析，并使用生物表达语言（BEL）表达的先验知识。我们提出了三个转录组数据集的哨声分析，使用一个计算机上可用的知识库。Whistle推断的机制与每个数据集的预期生物学一致。反向因果推理产生机制的见解，基因表达谱数据的解释是不同的，并补充使用本体或途径基因集的分析结果。这种逆向工程算法为疾病、药物作用和药物毒性模型的开发提供了一种证据驱动的方法。
Gene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action. We present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data. This methodology requires prior knowledge in the form of small networks that causally link a key upstream controller node representing a biological mechanism to downstream measurable quantities. These small directed networks are generated from a knowledge base of literature-curated qualitative biological cause-and-effect relationships expressed as a network. The small mechanism networks are evaluated as hypotheses to explain observed differential measurements. We provide a simple implementation of this methodology, Whistle, specifically geared towards the analysis of gene expression data and using prior knowledge expressed in Biological Expression Language (BEL). We present the Whistle analyses for three transcriptomic data sets using a publically available knowledge base. The mechanisms inferred by Whistle are consistent with the expected biology for each data set. Reverse Causal Reasoning yields mechanistic insights to the interpretation of gene expression profiling data that are distinct from and complementary to the results of analyses using ontology or pathway gene sets. This reverse engineering algorithm provides an evidence-driven approach to the development of models of disease, drug action, and drug toxicity.
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影响因子：
3.7
作者：
Deehan R;Maerz-Weiss P;Catlett NL;Steiner G;Wong B;Wright MB;Blander G;Elliston KO;Ladd W;Bobadilla M;Mizrahi J;Haefliger C;Edgar A
通讯作者：
Edgar A
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
5.8
作者：
Chindelevitch, Leonid;Ziemek, Daniel;Huang, Enoch S.
通讯作者：
Huang, Enoch S.
影响因子：
12.3
作者：
Newman, JC;Weiner, AM
通讯作者：
Weiner, AM
影响因子：
1.7
作者：
Magder, S.;Neculcea, I.;Sladek, R.
通讯作者：
Sladek, R.