Statistical and Machine Learning Methods to Address Biomedical Challenges for Integrating Multi-view Data

项目摘要 许多疾病是复杂的，异质的，影响身体多个器官的条件，并依赖于 包括遗传、细胞、分子和环境因素在内的几个因素之间的相互作用。因此 许多复杂疾病的发病机制仍然难以捉摸，并且缺乏治疗靶点，这并不奇怪。 传统的方法集中在少量的分子（例如，基因或代谢物）或单一类型的 数据（例如，临床或遗传）不能解决这种复杂性和异质性。综合或系统生物学 方法和网络分析可用于平衡来自多个源的数据的强度（例如， 基因组学，代谢组学，流行病学，临床数据），以实现对复杂的病理生物学的新见解 疾病最近的技术进步使得能够产生大量不同但相关的数据 丰富的信息，提供了非凡的机会，了解复杂的生物过程， 疾病和改变医学，但同时提出了重大的分析挑战，包括如何 有效地综合成千上万个数据点的信息，以确定重要的生物标志物， 具有作为治疗靶点的潜力。为了缓解这一问题，我们将开发和应用一套新颖，强大， 以及强大的统计和机器学习方法，用于整合和解释横截面和 多个来源的纵向数据。这些模型还将用于定义以下患者亚群： 根据不同来源的数据，有不同的疾病或需要不同的治疗方法。此外，本发明还 我们将利用网络理论的最新进展来模拟分子中复杂的多边关系， 来自多个来源的数据。所提出的方法将适用于几个公开的数据集和队列 确保我们可以将我们的工作推广到其他数据集和群组，从而增加长期影响 我们的研究。拟议的研究还将贡献有价值的统计和机器学习算法 这将广泛适用于来自多个来源和多个队列的数据，并将提供给 公共免费。

Project Summary Many diseases are complex, heterogeneous, conditions that affect multiple organs in the body and depend on the interplay between several factors that include genetic, cellular, molecular, and environmental factors. It is therefore not surprising that the pathogenesis of many complex diseases remain elusive, and therapeutic targets are lacking. The traditional approach that focus on a small number of molecules (e.g., genes or metabolites) or a single type of data (e.g., clinical or genetic) cannot address this complexity and heterogeneity. Integrative or systems biology approaches and network analysis can be used to leverage the strengths of data from multiple sources (e.g., genomics, metabolomics, epidemiology, clinical data) to achieve new insights into the pathobiology of complex diseases. Recent technological advances have enabled the production of vast amounts of diverse but related data with rich information that offer remarkable opportunities to understand biological processes involved in complex diseases and to transform medicine, yet at the same time present significant analytical challenges including how to effectively synthesize information from the tens of thousands of data points to identify important biomarkers with potential to serve as therapeutic targets. To alleviate this, we will develop and apply a suite of novel, robust, and powerful statistical and machine learning methods for the integration and interpretation of cross-sectional and longitudinal data from multiple sources. These models will also be used to define subpopulations of patients who have different prognoses or require different therapeutic approaches based on data from different sources. Further, we will make use of recent advances in network theory to model the complex multilateral relationships in molecular data from multiple sources. The proposed methods will be applied to several publicly available datasets and cohorts to ensure that we can generalize our work to other datasets and cohorts and thus increase the long-term impact of our research. The proposed research will also contribute valuable statistical and machine learning algorithms that will be broadly applicable to data from multiple sources and multiple cohorts and will be made available to the public free of charge.