Methods for Accurate and Efficient Discovery of Local Pathways.

准确有效地发现局部路径的方法。

基本信息

批准号：
9343088
负责人：
Constantin F. Aliferis
金额：
$ 15.9万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-01 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9343088
关键词：
Methods Accurate Efficient Discovery Local

项目摘要

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）：发现与人类复杂疾病有关的途径是生物医学研究的前沿。许多科学家对发现仅包含感兴趣表型或目标分子的直接原因和直接影响的局部因果路径特别感兴趣。在当前的项目中，我们提出了一个新的框架和方法，通过将高通量观测数据与有效的实验策略相结合，能够准确发现局部因果路径。该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路径的多样性。这种现象混淆了变量的因果作用，并导致当前所有因果发现算法的输出中存在大量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，考虑到了他们重要的资源限制和实验工作流程。因此，所提出框架的主要目标之一是最大限度地减少昂贵的湿实验室实验资源的使用，同时实现高发现准确性。拟议的项目扩展了我们之前的工作，我们研究了与数据一致的分子特征和因果路径的多重性现象，并提供了一系列新方法（称为 TIE*），可以从观测数据中证明并有效地发现所有特征表型。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分子变量的因果作用需要本文提出的新方法。我们假设，结合观察和实验数据来发现局部因果路径的新方法可以比现有的观察方法获得更高的发现准确性，同时比现有的实验方法使用更少的实验资源。简而言之，我们建议开发新的准确且实验有效的局部因果路径发现方法；在现实的计算机和真实的生物数据和途径中广泛评估新的和现有的方法；提高对这些方法的假设及其在高通量数据中的实用性的理解；并将这些方法应用于两个正在进行的一线生物医学项目，以产生并通过实验验证关于两种疾病的新见解。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与脂肪肝疾病相关的局部因果途径。