Causal Discovery Algorithms for Translational Research with High-Throughput Data

项目摘要 用于高通量数据翻译研究的因果发现算法 该项目的长期目标是为生物医学界提供下一代因果关系 便于发现疾病分子途径和致病原因以及预测的算法 来自高通量数据的生物标志物和分子签名。这些知识和方法是 对于更早和更准确的诊断和预后、个性化医疗和 合理的药物设计。 如果成功，拟议的研究将具有重大而广泛的方法论和实践意义。 跨生物医学多个领域的影响，主要关注和直接受益于 高通量诊断和个性化医疗。它将提供显著改进的 与生产分子相关的计算方法和更深层次的理论理解 用于新药的疾病和伴随线索的特征和理解机制。会的 为新的因果方法在其他类型的数据中的适用性提供证据。它将生成 对人类肺癌特定途径的洞察。它将加深我们的理解和解决方案 与组学数据中的罗生门效应有关。拟议的研究还将揭示可操作的 稳定性启发式的值。最后，这项研究将使国际研究界参与 解决与高吞吐量和其他相关的开放式计算因果发现问题 生物医学数据。 目的1.评价和表征生物标记物的几种新的因果算法 使用真实的、模拟的、 重新模拟和实验的数据集。通过以下方法研究方法的一般性 对非组学数据集的适用性。 目的2.评估和表征新的和最先进的因果算法 最先进的非因果和准因果算法。 目的3.系统研究罗生门效应在生物标志物和生物标记物中的应用 签名的多重性。 目的4.系统地研究稳定性启发式方法在多目标系统中的应用 因果发现。 目的5.寻找肺癌的新生物标志物、新途径和新假说。 目的6.通过国际因果发现竞赛得出新的解决方案。 目的7.传播调查结果。

Project Summary Causal Discovery Algorithms for Translational Research with High-Throughput Data The long-term goal of this project is to provide to the biomedical community next-generation causal algorithms to facilitate discovery of disease molecular pathways and causative as well as predictive biomarkers and molecular signatures from high-throughput data. Such knowledge and methods are necessary toward earlier and more accurate diagnosis and prognosis, personalized medicine, and rational drug design. If successful, the proposed research will have significant and wide methodological and practical implications spanning several areas of biomedicine with a primary focus and immediate benefits in high-throughput diagnostics and personalized medicine. It will provide significantly improved computational methods and deeper theoretical understanding related to producing molecular signatures and understanding mechanisms of disease and concomitant leads for new drugs. It will provide evidence about applicability of novel causal methods in other types of data. It will generate insights in specific pathways of lung cancer in humans. It will deepen our understanding and solutions to the Rashomon effect in ¿omics¿ data. The proposed research will also shed light on the operational value of the stability heuristic. Finally the research will engage the international research community to address open computational causal discovery problems relevant to high-throughput and other biomedical data. ¿ Aim 1. Evaluate and characterize several novel causal algorithms for biomarker selection, molecular signature creation and reverse network engineering using real, simulated, resimulated, and experimental datasets. Study generality of the methods by means of applicability to non-¿omics¿ datasets. ¿ Aim 2. Evaluate and characterize, novel and state of the art causal algorithms against state-of-the-art non-causal and quasi-causal algorithms. ¿ Aim 3. Systematically investigate the Rashomon effect as it applies to biomarker and signature multiplicity. ¿ Aim 4. Systematically investigate the utility of applying the stability heuristic for causal discovery. ¿ Aim 5. Derive novel biomarkers, pathways and hypotheses for lung cancer. ¿ Aim 6. Induce novel solutions through an international causal discovery competition. ¿ Aim 7. Disseminate findings.