III: Medium: High-Dimensional Interaction Analysis in Bio-Data Sets

发现数据集中属性之间的交互可以深入了解数据的底层结构，并解释属性之间的关系。本项目开发多学科方法，整合计算机科学、统计学和流行病学技术，以挖掘生物数据集中属性和表型（性状或类标签）之间的相互作用关系。具体而言，该项目开发了创新和统计上合理的方法，用于挖掘属性内部或属性与表型之间的新相互作用，以帮助确定生物学应用中的关键因素。特别是，新的分析方法可以使一系列复杂疾病的遗传和环境相互作用得以描述。该项目的研究活动还可以促进生物学、计算机科学和统计学的融合，这对许多应用都具有重要意义。该项目将制定各种指标，以便在多维组合空间中进行有效的修剪和搜索，以确定重要的交互关系。这使得构建基于搜索的树或识别高度相关的子空间的高效方法能够检测到有意义的局部交互，这些交互可能在整个数据集中并不重要，但与数据子集上的特征有强烈的交互。这样就可以比较来自多个不同组的数据，例如基于年龄、种族或其他属性的数据。重要的是找到不同群体中共同的和不同的相互作用，以便为目标群体制定有效的方法。开发的方法将通过捕获联合矩阵分解或深度学习模型中的共性和差异，同时检测多个组中属性之间的复杂相互作用。这些方法在生物学应用方面非常强大，例如检测导致乳腺癌的基因-基因相互作用和基因-环境相互作用。从经济学、社会学、物理学到制药科学，相互作用的概念在许多科学学科中也无处不在，而且很重要。本项目开发的新方法和分析工具对于发现与表型标签相关或没有表型标签的属性之间的任何相互作用关系非常有用。这些方法和工具是通用的，适用于各种应用程序。

Discovering interactions between the attributes in a data set provides insight into the underlying structure of the data and explains the relationships between the attributes. This project develops multi-disciplinary approaches that integrate computer science, statistics, and epidemiology techniques to mine interaction relationships among attributes and phenotypes (traits or class labels) in biological data sets. Specifically, this project develops innovative and statistically sound methodologies for mining novel interactions within attributes or between attributes and phenotypes to help identify critical factors in biological applications. In particular, the novel analysis methods can enable the genetic and environmental interactions underlying a range of complex diseases to be delineated. The research activities of this project can also promote the integration of biology, computer science, and statistics, which is highly significant to many applications. The project will formulate various metrics that enable efficient pruning and searching in the multi-dimensional combinatorial space for identifying significant interaction relationships. This enables highly effective approaches that build search-based trees or identify highly correlated subspaces to detect meaningful local interactions that may not be significant considering the whole data sets but are strongly interacted with traits on a subset of data. This enables comparison of data from multiple different groups such as based on age, race, or other properties. It is important to find both common and different interactions in different groups so that effective methods can be developed for targeted groups. The methods developed will detect complex interactions between attributes in multiple groups simultaneously by capturing both their commonalities and differences in joint matrix factorization or deep learning models. These approaches are remarkably powerful for biological applications, such as detecting gene-gene interactions and gene-environmental interactions that lead to breast cancer. The concept of interaction is also ubiquitous and important in many scientific disciplines ranging from economics, sociology and physics, to the pharmaceutical sciences. The novel approaches and analysis tools developed in this project are useful for finding out any interaction relationships between attributes associated with phenotype labels or without phenotype labels. These approaches and tools are general and are applicable to a variety of applications.