Methods for Accurate and Efficient Discovery of Local Pathways.

准确有效地发现局部路径的方法。

• 批准号: 8714055
• 负责人: Constantin F. Aliferis
• 金额: $ 27.72万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8714055
• 关键词: Accounting; Acute; Affect; Algorithms; Area; Biological; Biomedical Research; Cancer Prognosis; Classification; Communities; Complex; Computer Simulation; Data; Data Set; Databases; Development; Diagnosis; Dimensions; Disease; Disease Progression; Evaluation; Family; Fatty Liver; Gene Chips; Genes; Goals; Human; Laboratories; Liver diseases; Lymph Node Involvement; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Medicine; Methods; Molecular; Molecular Profiling; Neoplasm Metastasis; Output; Pathogenesis; Pathway interactions; Performance; Pharmacotherapy; Phenotype; Predisposition; Proteomics; Regulator Genes; Relative (related person); Research; Research Personnel; Research Project Grants; Resources; Rheumatoid Arthritis; Role; Saccharomyces cerevisiae; Sample Size; Scientist; Series; Structure; Techniques; Validation; Virus Diseases; Work; cell growth; comparative; computer framework; design; fighting; genome wide association study; human disease; improved; insight; interest; knockout gene; malignant breast neoplasm; next generation; novel; research study; respiratory; response; tumor; yeast genome

DESCRIPTION (provided by applicant): Discovery of pathways that implicate complex diseases in humans is at the forefront of biomedical research. Many scientists are specifically interested in discovery of local causal pathways that contain only direct causes and direct effects of the phenotype or target molecule of interest. In the current project we propose a new framework and methods to enable accurate discovery of local causal pathways by integrating high-throughput observational data with efficient experimentation strategies. At the core of this framework are computational causal discovery methods that account for multiplicity of causal pathways consistent with the data. This phenomenon confounds the causal role of the variables and leads to a large number of false negative and false positive predictions in the output of all current causal discovery algorithms. The framework is designed specifically for biomedical researchers by taking into consideration their significant resource limitations and experimental workflow. For this reason, one of the primary objectives of the proposed framework is to minimize the use of costly wet-laboratory experimental resources while achieving high discovery accuracy. The proposed project extends our prior work, where we have studied the phenomenon of multiplicity of molecular signatures and causal pathways consistent with the data and provided a family of new methods (called TIE*) that can provably and efficiently discover from observational data all signatures of the phenotype. Even though TIE* methods can extract multiple signatures of the disease, determining its local causal pathway and causal role of the involved molecular variables requires new methods that are proposed herein. We hypothesize that the new methods for discovery of local causal pathways from a combination of observational and experimental data can achieve higher discovery accuracy than existing observational approaches while using fewer experimental resources than existing experimental approaches. Briefly, we propose to develop new accurate and experimentally efficient local causal pathway discovery methods; extensively evaluate new and existing methods both in realistic in-silico and real biological data and pathways; improve understanding of assumptions of these methods and their practicality in high-throughput data; and apply these methods to two ongoing front-line biomedical projects to generate and experimentally validate new insights about two diseases. The first biomedical project aims to understand molecular mechanisms leading to metastasis and lymph node involvement from locally advanced breast cancer. The second biomedical project aims to unravel the fatty liver disease-related local causal pathways.

描述（由申请人提供）：发现涉及人类复杂疾病的途径是生物医学研究的前沿。许多科学家特别感兴趣的是发现局部因果途径，这些途径仅包含感兴趣的表型或靶分子的直接原因和直接影响。在目前的项目中，我们提出了一个新的框架和方法，使当地的因果通路的准确发现，通过整合高通量的观测数据与有效的实验策略。这个框架的核心是计算因果发现方法，它解释了与数据一致的因果途径的多样性。这种现象混淆了变量的因果作用，并导致在所有当前因果发现算法的输出中出现大量的假阴性和假阳性预测。该框架是专门为生物医学研究人员设计的，考虑到他们的重大资源限制和实验工作流程。出于这个原因，所提出的框架的主要目标之一是最大限度地减少使用昂贵的湿实验室实验资源，同时实现高的发现精度。拟议的项目扩展了我们先前的工作，在那里我们研究了与数据一致的分子签名和因果途径的多重性现象，并提供了一系列新方法（称为TIE*），可以证明和有效地从观察数据中发现表型的所有签名。尽管TIE* 方法可以提取疾病的多个特征，但确定其局部因果途径和所涉及的分子变量的因果作用需要本文提出的新方法。我们假设，从观察和实验数据的组合中发现局部因果通路的新方法可以实现比现有的观察方法更高的发现准确性，同时使用比现有的实验方法更少的实验资源。简而言之，我们建议开发新的准确和实验有效的本地因果通路发现方法;广泛评估新的和现有的方法，无论是在现实的计算机和真实的生物数据和途径;提高理解这些方法的假设及其在高通量数据中的实用性;并将这些方法应用于两个正在进行的前线生物医学项目，以产生和实验验证有关两种疾病的新见解。第一个生物医学项目旨在了解导致局部晚期乳腺癌转移和淋巴结受累的分子机制。第二个生物医学项目旨在揭示脂肪肝疾病相关的局部因果途径。