Convex space learning for synthetic data generation on clinical tabular datasets

用于临床表格数据集上的合成数据生成的凸空间学习

• 批准号: 515800538
• 负责人: Professor Dr. Olaf Wolkenhauer
• 金额: --
• 依托单位: Lehrstuhl für Systembiologie und Bioinformatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/515800538?language=en
• 关键词: Convex; space; learning; synthetic; data

Synthetic data generation is gaining prominence in biomedical research in solving practical problems: personalization; underrepresentation of groups in clinical trials; data privacy hindering sharing of data among institutions etc. Synthetic data generation using deep generative networks for medical images is a booming research field. For image datasets, there is a perceptional advantage, in the sense, that one can visually judge how realistic the synthetic image is, just by looking at it. However, in biomedical science, tabular datasets are a very common way of storing patient data, and for such data the advantage of visual perception is limited. Since 2017, researchers have focused on developing deep generative models for tabular datasets. Over the last three years, we have developed expertise in tabular synthetic data generation to solve the problem of imbalanced classification. We developed multiple algorithms in the domain of oversampling-driven imbalanced classification and tested their applicability to biological problems such as rare-cell annotation from single-cell transcriptomics data. From our studies emerged the idea of convex space learning, whose theoretical foundations were also explored in our studies. With our newest convex space learning model ConvGeN, we were able to improve classification on tabular imbalanced datasets using synthetic sample generation, compared to the state-of-the-art deep generative algorithms designed for tabular datasets. Synthetic samples generated using ConvGeN can approximate feature-wise statistical distributions better compared to existing deep generative algorithms for tabular datasets since the synthetic samples from ConvGeN fix feature-wise means in tabular data while learning appropriate feature-wise higher-order moments in a non-linear iterative fashion. We argue that convex space learning has extensive potential outside the domain of imbalanced classification that we have explored so far. We propose to extend our model ConvGeN, enabling it to generate synthetic tabular data outside the context of data imbalance. Furthermore, we propose to investigate the potential use of the synthetic data generated using convex space learning for several applications of machine learning in the clinical domain such as patient stratification, classification, regression problems, etc. The goal is to establish whether a given machine learning workflow involving synthetic data generation can produce similar enough performance as using real data, e.g. in patient stratification. Finally, we propose to use the developed algorithm for synthetic sample generation in real-life clinical problems to solve issues like privacy preservation in association with our clinical partners.

合成数据生成在生物医学研究中越来越突出，以解决实际问题：个性化;临床试验中群体代表性不足;数据隐私阻碍机构之间的数据共享等。对于图像数据集，有一个感知的优势，在这个意义上说，一个人可以直观地判断合成图像的真实性，只是通过看它。然而，在生物医学科学中，表格数据集是一个非常常见的方式存储病人的数据，和这样的数据的视觉感知的优势是有限的。自2017年以来，研究人员一直专注于为表格数据集开发深度生成模型。在过去的三年里，我们已经开发了表格合成数据生成方面的专业知识，以解决不平衡分类的问题。我们在过采样驱动的不平衡分类领域开发了多种算法，并测试了它们对生物学问题的适用性，例如来自单细胞转录组学数据的稀有细胞注释。从我们的研究中出现了凸空间学习的想法，其理论基础也在我们的研究中进行了探索。通过我们最新的凸空间学习模型ConvGeN，与为表格数据集设计的最先进的深度生成算法相比，我们能够使用合成样本生成来改进表格不平衡数据集的分类。与现有的表格数据集深度生成算法相比，使用ConvGeN生成的合成样本可以更好地近似特征统计分布，因为来自ConvGeN的合成样本修复了表格数据中的特征均值，同时以非线性迭代方式学习适当的特征高阶矩。我们认为，凸空间学习具有广泛的潜力领域以外的不平衡分类，我们已经探索到目前为止。我们建议扩展我们的模型ConvGeN，使其能够在数据不平衡的背景下生成合成表格数据。此外，我们建议研究潜在的使用凸空间学习的机器学习在临床领域的几个应用程序，如患者分层，分类，回归问题等，我们的目标是建立一个给定的机器学习工作流程，涉及合成数据生成是否可以产生足够的性能相似，使用真实的数据，例如在患者分层的合成数据。最后，我们建议在现实生活中的临床问题中使用开发的合成样本生成算法来解决与我们的临床合作伙伴相关的隐私保护等问题。