Network-based machine learning framework for for data integration in medical applications

基于网络的机器学习框架，用于医疗应用中的数据集成

• 批准号: RGPIN-2014-04442
• 负责人: Goldenberg, Anna
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634866
• 关键词: Network; based; machine; learning; framework

Recent technological advances have made it possible to assemble very large data collections – Big Data. Aside from the sheer scale, we now have access to multiple data types each describing the same phenomena in their own way. For example, in computer vision, it is now common to combine unlabeled images with text to label images more accurately. In biology and medicine, it has recently become cost-effective to collect genomic, transcriptomic, epigenetic, microbiome and other measurements that describe the state of cells and human bodies in health and disease. It has thus become essential to develop robust and efficient machine learning methods that can integrate multiple data types to gain deeper understanding of the phenomena measured by these data. Majority of existing integrative methods have limitations, e.g. they often require significantly more samples than features (not readily available in biological and medical applications); do not scale to the large number of available features requiring ad hoc feature pre-selection (Shen et al, 2009); do not deal with missing data and noise requiring substantial data pre-processing. In human studies, especially in childhood diseases, where the goal is to combine multiple types of measurements to understand disease mechanisms and reasons for phenotypic heterogeneity, the number of patients is very limited, whereas the number of measurements available for each patient is very large. Majority of existing methods are not applicable in this scenario. It is thus essential to ensure that new methods for biological and clinical data integration are scalable and robust to small sample sizes and many features. Together with my advisees we have developed a robust unsupervised approach to integrate multiple types of biological data, called Patient Network Fusion (PNF) (Wang et al, 2013) that addresses the issues above. Our results on five cancers show that we obtain more clinically relevant subtypes than those previously reported. We believe that networks in general and our approach for patient network fusion in particular set a perfect foundation for the comprehensive integrative framework that we are proposing to build in the course of the next five years. Our research program addresses the problem of data integration from several angles. Building on our recent successes we will develop methods to integrate more data types, specifically, low-signal-to-noise ratio data, such as single nucleotide polymorphisms, into our network-based framework. We will also extend non-negative matrix factorization to perform data integration of biological data using network regularization. These developments will serve as a broad base for the integration framework that will be tested on novel data obtained through our established clinical collaborations. Further, we will investigate several methods for integrative feature selection, i.e. identifying a small set of features stemming from multiple data types that explain majority of the variation in the data. Such methods are essential in shedding light onto the inner workings of biological processes and disease mechanisms. I work in close collaboration with clinicians at the Hospital for Sick Children and internationally to make sure that the methods are applied to real data and can be used and evaluated by clinicians in the process of their development. By working with a selected set of end-user collaborators, we will evaluate and refine our machine learning methods and user-interfaces, and ultimately develop a system that will impact the larger community of researchers interested in biological and medical data analysis.

最近的技术进步使得组装非常大的数据集合（大数据）成为可能。除了规模庞大之外，我们现在还可以访问多种数据类型，每种数据类型都以自己的方式描述相同的现象。例如，在计算机视觉中，现在通常将未标记的图像与文本结合起来以更准确地标记图像。在生物学和医学领域，收集基因组、转录组、表观遗传学、微生物组和其他描述细胞和人体健康和疾病状态的测量数据最近变得具有成本效益。因此，开发强大且高效的机器学习方法变得至关重要，这些方法可以集成多种数据类型，以更深入地了解这些数据测量的现象。大多数现有的综合方法都有局限性，例如它们通常需要比特征多得多的样本（在生物和医学应用中不容易获得）；不要扩展到需要临时特征预选的大量可用特征（Shen 等人，2009）；不处理需要大量数据预处理的丢失数据和噪声。在人类研究中，特别是在儿童疾病中，目标是结合多种类型的测量来了解疾病机制和表型异质性的原因，患者的数量非常有限，而每个患者可用的测量数量却非常大。大多数现有方法不适用于这种情况。因此，必须确保生物和临床数据集成的新方法对于小样本量和许多特征具有可扩展性和鲁棒性。我们与我的顾问一起开发了一种强大的无监督方法来整合多种类型的生物数据，称为患者网络融合（PNF）（Wang 等人，2013），可以解决上述问题。我们对五种癌症的结果表明，我们获得了比之前报道的更多的临床相关亚型。我们相信，总体网络，特别是我们的患者网络融合方法，为我们提议在未来五年内建立的全面整合框架奠定了完美的基础。我们的研究计划从多个角度解决数据集成问题。在我们最近取得的成功的基础上，我们将开发将更多数据类型（特别是低信噪比数据，例如单核苷酸多态性）集成到我们基于网络的框架中的方法。我们还将扩展非负矩阵分解以使用网络正则化来执行生物数据的数据集成。这些进展将作为集成框架的广泛基础，该框架将根据通过我们建立的临床合作获得的新数据进行测试。此外，我们将研究几种综合特征选择的方法，即识别源自多种数据类型的一小组特征，这些特征可以解释数据中的大部分变化。这些方法对于揭示生物过程和疾病机制的内部运作至关重要。我与病童医院和国际上的临床医生密切合作，以确保这些方法应用于真实数据，并可供临床医生在其开发过程中使用和评估。通过与一组选定的最终用户合作者合作，我们将评估和完善我们的机器学习方法和用户界面，并最终开发出一个系统，该系统将影响对生物和医学数据分析感兴趣的更广泛的研究人员社区。