Incorporating molecular network knowledge into predictive data-driven models

基于机器学习(ML)和最近的深度学习(DL)的现代计算技术是 在实现精准医疗行动中发挥关键作用。然而，迫切需要 系统地将这些强大的数据驱动技术与先前的分子网络知识相结合，使 更准确的预测模型，同时也令人满意地解释了它们在机理方面的预测 潜在的复杂特征和疾病。我建议使用生物学领域的特定知识， 计算以解决三个突出问题：1)如何预测与数百万个 可公开获取的样本？2)这些样本可以附加什么分子/细胞功能？3)如何 我们能否将这些发现从人类数据转换到一个模型物种，然后再转换回来？网络受限的深度 元数据归因的学习：大多数多因素表型是组织依赖的和明显的 不同的年龄、性别和种族。然而，大多数可公开获得的基因组数据缺乏 这些标签。我将开发一种网络引导的方法来预测样本的缺失元数据 通过设计新颖的数据驱动模型来表达简档，其中模型架构和/或 输入数据受到潜在基因网络的限制。网络引导下的基因组功能分析 数据：高通量实验通常会产生难以解释的感兴趣基因列表。 功能丰富分析(FEA)是一种将功能意义赋予实验的有力工具 通过将一组基因总结为一组途径/过程来实现的。然而，标准的有限元分析是有限的 由于对基因功能的不完全了解，缺乏潜在基因网络的背景，以及 表达式数据。我将通过开发一种网络引导的方法来解决这些限制，该方法联合捕获 基因，它们的相互作用，以及它们已知的生物路径/过程，形成一个共同的，低维的 便于通过比较实验基因之间的距离来得出生物学意义的空间 SET和感兴趣的路径/过程。联合多物种基因组数据分析与知识 转移：特别是寻找最优模型系统，用于基于遗传的后续研究 从人体实验中获得的签名是具有挑战性的，因为遗传网络可能会非常不同 从一个物种到另一个物种。我建议使用数据驱动模型来嵌入由以下内容组成的异类网络 将人类基因和模式物种基因放入共同的低维空间，更好地比较遗传 两个(甚至多个)物种之间的签名。我将把这些方法应用于三个具体的任务，但我 强调，这项研究的结果将可以转移到任何其他复杂的生物学问题 基因/蛋白质相互作用是主要组成部分。我身边有一支很棒的支持团队 制定了强有力的专业发展计划。F32奖学金提供的自由和支持 将有助于我实现成为一个独立研究小组的教授的目标。

Modern computational techniques based on machine-learning (ML) and, more recently, deep-learning (DL) are playing a critical role in realizing the precision medicine initiative. However, there is a critical need to systematically combine these powerful data-driven techniques with prior molecular network knowledge to make more accurate predictive models while also satisfactorily explaining their predictions in terms of mechanisms underlying complex traits and diseases. I propose to use domain specific knowledge from biology and computing to tackle three outstanding problems: 1) how to predict missing labels associated with millions of publicly available samples? 2) what molecular/cellular function can be attached to these samples and 3) how can we translate the findings from human data to a model species and back? Network-constrained Deep Learning for Metadata Imputation: Most multifactorial phenotypes are tissue dependent and manifest differently depending on age, sex, and ethnicity. However, a majority of publicly-available genomic data lack these labels. I will develop a network-guided approach to predict missing metadata of samples based on their expression profiles by designing novel data-driven models where the model architecture and/or structure of the input data are constrained by an underlying gene network. Network-guided Functional Analysis of Genomic Data: High-throughput experiments often generate lists of genes of interest that are hard to interpret. Functional enrichment analysis (FEA) is a powerful tool that attaches functional meaning to an experimental set of genes by summarizing them into sets of pathways/processes. However, standard FEA analysis is limited by incomplete knowledge of gene function, lack of context of the underlying gene network, and noise in expression data. I will address these limitations by developing a network-guided approach that jointly captures genes, their interactions, and their known biological pathways/processes into a common, low-dimensional space that facilitates deriving biological meaning by comparing the distance between the experimental gene set and the pathway/process of interest. Joint Multi-Species Genomic Data Analysis and Knowledge Transfer: In particular, finding the optimal model system to use in a follow-up study based on genetic signatures derived from human experiments is challenging because genetic networks can be quite different from species to species. I propose to use data-driven models to embed heterogeneous networks comprised of human genes and model species genes into a common, low-dimensional space to better compare genetic signatures between two (or even multiple) species. I will apply these methods to three specific tasks, but I emphasize that the results of this study will be transferable to any other biological problem where complex gene/protein interactions are a major component. I have surrounded myself with a great support team and developed a strong professional development plan. The freedom and support provided by the F32 fellowship will be instrumental in achieving my goal of becoming a professor with an independent research group.