ABI Innovation: A Probabilistic Approach to Meta-Analysis of Biological Network Interface

基因不是单独工作的，而是在一个复杂的相互作用网络中调节基本的细胞过程。 生物网络推理已经成为现代生物学中的一个关键分析工具，使科学家能够更好地了解基础生物学，如进化，分子生物学和遗传学。网络推理算法在从分子数据理解基因网络方面发挥了重要作用，但受到网络结构的大搜索空间和统计能力不足的数据的限制。该项目的目标是开发一个基于统计和机器学习技术的计算框架，用于有效地整合多个异构数据集，以准确地推断基因网络。所开发的方法将被应用于回答重要的生物学问题，例如：在酵母物种的进化过程中，基因网络是如何重塑的？基因和蛋白质是如何相互作用的？哪些基因含有影响重要生物学性状的致病序列变异？该项目预计将大大提高网络学习算法在各种应用中的适用性，特别是那些样本量较小的应用。所开发的方法的实施将公开提供，这可以帮助许多其他生物学家在他们的研究问题中研究基因网络。该项目具有跨学科性质，通过项目课程和外联活动，重点强调跨学科教育。通过增加计算机科学学生的数量来解决生物学问题，它将对生物学领域的发展产生长期影响。从高通量生物数据中进行网络推理，极大地促进了我们对分子生物学，进化和遗传学的认识。它的主要缺点是，由于网络结构的搜索空间呈指数级大，单个数据集提供的样本大小往往不足以获得有效的推理结果。然而，简单地附加来自不同研究的数据集不太可能成功，因为在许多情况下，它们包含不同的变量和过度异质的样本。该项目的目标是开发一种创新的概率方法，通过联合建模由这些数据集表示的一个或多个网络来集成多个异构数据集。该项目解决了如何整合包含异质样本（目标1），不同变量集（目标2）和根本不同类型的测量（目标3）的数据集的问题。在每个目标中开发的算法应用于以下问题：1）学习一个网络模型，可以代表基因调控网络在酵母属物种的进化重新布线; 2）学习一个联合潜在的网络模型，可以推断出一个非常高维的网络基础生物膜表型在S。3）从多个表达数据集中识别基因网络中的枢纽，指导酿酒酵母数量性状基因座的研究。啤酒。

Genes do not work alone, but rather in an intricate network of interactions to regulate fundamental cellular processes. Biological network inference has become a key analysis tool in modern biology allowing scientists to gain a better understanding of basic biology such as evolution, molecular biology and genetics. Network inference algorithms have played significant roles in understanding gene networks from molecular data but are limited by the large search space of the network structure and data insufficient for statistical power. The goal of this project is to develop a computational framework, based on statistical and machine learning techniques, for effectively integrating multiple heterogeneous data sets to infer gene networks accurately. The developed methods will be applied to answer important biological questions, such as: How is a gene network reshaped in the evolutionary process of yeast species?, How do genes and proteins interact with one another underlying a certain biological trait?, and Which genes contain causative sequence variations that influence important biological traits?. This project is expected to dramatically increase the applicability of network learning algorithms in a wide variety of applications, especially those with smaller sample sizes. The implementation of the developed methods will be made publicly available, which can help many other biologists to study gene networks in their research problems. This project is interdisciplinary in nature and has significant emphasis on interdisciplinary education, through project courses and outreach activities. It will have a long-term effect of advancing the field of biology, by increasing the number of students in computer science inspired to solve biology problems.Network inference from high-throughput biological data has significantly contributed to advancing our knowledge of molecular biology, evolution and genetics. Its major drawback is that, due to the exponentially large search space of the network structure, the sample size provided by a single dataset is often not large enough to obtain valid inference results. However, simply appending datasets from different studies is unlikely to be successful, because in many cases they contain different variables and overly heterogeneous samples. The goal of this project is to develop an innovative probabilistic approach to integrate multiple heterogeneous datasets, by jointly modeling one or more networks represented by these datasets. This project addresses the problem of how to integrate datasets containing heterogeneous samples (Aim 1), different sets of variables (Aim 2) and fundamentally different types of measurements (Aim 3). The developed algorithms in each Aim is applied to the following problems: 1) learning a network model that can represents evolutionary rewiring of gene regulatory networks across Saccharomyces species; 2) learning a joint latent network model that can infer a very high-dimensional network underlying biofilm phenotypes in S. cerevisiae by combining datasets with different variables; and 3) identifying hubs in the gene network from many expression datasets, to guide quantitative trait loci studies in S. cerevisiae.