Networks for tissue-specific phenotype prediction from genomes

网络科学擅长于对系统组织不同层次的关系进行建模。一个新兴的研究领域是从多细胞生物的基因组数据预测表型。这种情况下使用相似网络，其中节点是样本，边缘是由一种基因组数据的成对相似性加权。相似性网络提供了一个概念上直观的框架，通过将每个数据转换为样本相似性的公共空间来集成异构数据。它们通过连接优于聚类和分类，因为它们在每个数据层中保留了相关结构。分类任务使用机器学习在复杂的数据中找到可推广的模式，但是使模型可解释，这是机械洞察力所需要的，仍然是一个开放的挑战。我们之前开发了一个基于网络的分类器，它集成了多组学数据，并将推荐系统(如Netflix中使用的推荐系统（“查找像这样的电影”）与表现型（“查找像这样的样本”）相适应。除了优异的性能外，该算法netDx还提供了创建生物可解释特征的能力，例如路径。netDx目前缺乏深度学习框架的几个优势，深度学习框架是一个快速发展的机器学习领域，具有卓越的非线性判别能力和将预训练模型用于新应用的能力。另外，在模型确定预测基因或途径后，基因优先级用于选择分子进行表型扰动实验。目前的优先排序方法对分子相互作用网络中的组织特异性差异不可知，这增加了脱靶效应的风险。多模态基因组数据的可解释表型预测。我们将使用图卷积网络使netDx分类器适应深度学习。我们将评估转录组学、表观基因组学和染色质可及性数据的特征设计策略，以确定从编码和非编码基因组创建可解释预测因子的一般原则。我们将评估将预训练模型从一个组织转移到相关组织或相关物种的能力。药物优先排序的组织特异性扰动模型。我们将开发一种基于网络的基因优先排序算法，该算法使用组织和细胞特异性分子相互作用网络来寻找直接和间接的药物靶点，并优化候选基因的组织特异性。意义，影响，应用，培训机会：我们的方法将适用于任何涉及从多组学数据预测表型的问题，或预测药物扰动的组织特异性影响。这项工作将推进组织特异性基因组调控如何影响表型的知识。该研究项目将为加拿大HQP提供机器学习、网络科学和生物系统可解释预测建模方面的培训机会。

Network science excels at modelling relationships at different levels of systems organization. An emerging area of research is in predicting phenotype from genomic data in multicellular organisms. This context uses similarity networks, where nodes are samples and edges are weighted by pairwise similarity for a type of genomic data. Similarity networks provide a conceptually intuitive framework to integrate heterogeneous data by converting each to the common space of sample similarities. They outperform clustering and classification by concatenation, as they preserve correlation structures in each data layer. Classification tasks use machine learning to find generalizable patterns in complex data, but making models interpretable, needed for mechanistic insight, remains an open challenge. We previously developed a network-based classifier that integrates multi-'omic data and adapts recommender systems such as those used in Netflix ("find movies like this one"), to phenotype ("find samples like this one"). In addition to excellent performance, this algorithm, netDx, provides the ability to create biologically-interpretable features, such as pathways. netDx currently lacks several advantages of the deep learning framework, a fast-growing area of machine learning with superior non-linear discriminability and the ability to use pre-trained models for new applications. Separately, after a model identifies predictive genes or pathways, gene prioritization is used to select molecules for phenotypic perturbation experiments. Current prioritization methods are agnostic to tissue-specific differences in molecular interaction networks, which increase risk of off-target effects. Interpretable phenotype prediction from multi-modal genomic data. We will adapt the netDx classifier to deep learning, using graph convolutional networks. We will evaluate feature design strategies for transcriptomic, epigenomic and chromatin accessibility data to identify general principles for creating interpretable predictors from the coding and non-coding genome. We will evaluate the ability to transfer pre-trained models from one tissue to a related tissue, or a related species. Tissue-specific perturbation modeling for drug prioritization. We will develop a network-based gene prioritization algorithm that uses tissue- and cell-specific molecular interaction networks to find direct and indirect drug targets, and optimize tissue-specificity of candidate genes. Significance, impact, applications, training opportunities: Our methods will be applicable to any problem involving phenotype prediction from multi-`omic data, or to predicting tissue-specific impact of pharmacological perturbation. This work will advance knowledge of how tissue-specific genome regulation impacts phenotype. This research program will provide training opportunities for Canadian HQP in machine learning, network science, and in interpretable predictive modeling of biological systems.