Causal Discovery Algorithms for Translational Research with High-Throughput Data

用于高通量数据转化研究的因果发现算法

基本信息

批准号：
7643514
负责人：
Constantin F. Aliferis
金额：
$ 0.74万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-08-01 至 2009-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7643514
关键词：
AKT1 gene AKT2 gene AKT3 gene Address Affect Algorithms Area Arts Benchmarking Bioinformatics Biologic Characteristic Biological Markers Biology Biometry Book Chapters Books Cancer cell line Causations Characteristics Clinic Clinical Clinical Data Clinical Trials Communities Computational Biology Computer software Computing Methodologies Consultations Data Data Set Depth Development Diagnosis Diagnostic Dimensions Discipline Disease Drug Design Educational process of instructing Educational workshop Engineering Ensure Epidermal Growth Factor Receptor European Evaluation Event Excision Gene Expression Gene Targeting Genomics Goals Gold Healthcare Hereditary Disease Home environment Human Human Cell Line Inferior Information Retrieval Institution International Knowledge Laboratories Lead Learning Light Localized Machine Learning Malignant neoplasm of lung Marker Discovery Medicine Methods Modality Molecular Molecular Profiling Neighborhoods Noise Numbers Online Systems Outcome Output Paper Pathway interactions Peer Review Performance Pharmaceutical Preparations Process Proteomics Protocols documentation Public Domains Publishing Quality Control Random Allocation Randomized Rate Research Research Personnel Research Proposals Role Sample Size Sampling Schedule Score Services Simulate Solutions Standards of Weights and Measures Structure Testing Text Thinking Tissues Translational Research Variant Work base c-erbB-1 Proto-Oncogenes clinically relevant computer based statistical methods computer science contextual factors coping data mining design drug development heuristics human data human tissue improved innovation insight journal article member new technology next generation novel novel diagnostics outcome forecast reconstruction research study software systems symposium theories tool

项目摘要

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.

项目概要用于高通量数据转化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促进发现疾病分子途径、病因和预测的算法这些知识和方法是来自高通量数据的生物标志物和分子特征。对于更早、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其影响涵盖生物医学的多个领域，其主要重点和直接效益是高通量诊断和个性化医疗将提供显着改进。与生产分子相关的计算方法和更深入的理论理解疾病的特征和理解机制以及新药的伴随线索。提供有关新颖因果方法在其他类型数据中的适用性的证据。对人类肺癌特定途径的见解将加深我们的理解和解决方案。的罗生门效应 ¿组学??拟议的研究还将揭示操作的情况。最后，该研究将吸引国际研究界的参与。解决与高通量和其他相关的开放计算因果发现问题生物医学数据。 ¿目标 1. 评估和表征生物标志物的几种新颖因果算法使用真实、模拟、选择、分子签名创建和逆向网络工程通过重新模拟和实验数据集研究方法的通用性。适用于非 ¿组学??数据集。 ¿目标 2. 评估和表征新颖且最先进的因果算法最先进的非因果和准因果算法。 ¿目标 3. 系统地研究罗生门效应应用于生物标志物和签名多重性。 ¿目标 4. 系统地研究应用稳定性启发式的效用因果发现。 ¿目标 5. 得出肺癌的新生物标志物、途径和假设。 ¿目标 6. 通过国际因果发现竞赛推出新颖的解决方案。 ¿目标 7. 传播调查结果。