Coordination Funds

协调基金

• 批准号: 498591399
• 负责人: Professorin Dr. Sonja Greven
• 金额: --
• 依托单位: Lehrstuhl für Statistik
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498591399?language=en
• 关键词: Coordination; Funds

High-throughput measurements in the biomedical sciences such as stacks of images, genome sequences or time-series constitute structured data that are characterized by their inherent dependencies between measurements, often non-vectorial nature and the presence of confounding influences and sampling biases. For example, population structure, systematic measurement artifacts, non-independent sampling or different group age distributions can lead to spurious results if not accounted for. Deep learning excels in many applications on structured data due to the ability to capture complex dependencies within and between inputs and outputs, allowing for accurate prediction. Despite recent advances in explainable artificial intelligence and Bayesian neural networks, deep learning still has limitations with respect to its assessment of uncertainty, interpretability, and validation. These, however, are important components in order to go beyond prediction towards understanding the underlying biology. To this end, statistics has traditionally been used in the biomedical sciences due to interpretable model output and statistical inference, which i.a. provides quantification of uncertainty, corrections for confounding and testing of hypotheses with statistical error control. Methods from classical statistics, however, have limitations in their modelling flexibility for structured data and their ability to capture complex non-linearities in a data-driven way.In this research unit we bring together experts from machine learning and statistics with a track record in biomedical applications to address the following overarching objectives:(O1) to integrate deep learning and statistics to improve interpretability, uncertainty quantification and statistical inference for deep learning, and to improve modeling flexibility of statistical methods for structured data. In particular, we will develop methods that provide statistical inference for structured data by quantification of uncertainty, testing of hypotheses and conditioning on confounders, and that improve explanations of structured data through hybrid statistical and deep learning models, population- and distribution-level explanations, and robust sparse explanations.(O2) to create a feedback loop between this methods development and biomedical applications, where we account for the needs in the analysis of the data when developing new methods and generate biomedical insights from applications of the developed methods to the data. Applications include analysis of MRI, fMRI and microscopy images, longitudinal disease progression modeling, DNA sequence analysis, and genetic association studies.

生物医学科学中的高通量测量，如图像堆栈、基因组序列或时间序列，构成结构化数据，其特点是测量之间固有的依赖关系，通常是非矢量性质，存在混淆影响和抽样偏差。例如，如果不加以考虑，人口结构、系统测量伪像、非独立抽样或不同的群体年龄分布可能导致虚假的结果。深度学习在结构化数据的许多应用中表现出色，因为它能够捕获输入和输出内部和之间的复杂依赖关系，从而实现准确的预测。尽管最近在可解释的人工智能和贝叶斯神经网络方面取得了进展，但深度学习在评估不确定性、可解释性和有效性方面仍然存在局限性。然而，这些都是重要的组成部分，以便超越预测，了解潜在的生物学。为此目的，由于可解释的模型输出和统计推断，统计学传统上被用于生物医学科学，它提供了不确定性的量化、混淆的纠正和统计误差控制的假设检验。然而，经典统计方法在结构化数据的建模灵活性和以数据驱动的方式捕获复杂非线性的能力方面存在局限性。在这个研究单元中，我们汇集了机器学习和统计学方面的专家，他们在生物医学应用方面有着良好的记录，以解决以下总体目标：(1)整合深度学习和统计学，以提高深度学习的可解释性、不确定性量化和统计推断，并提高结构化数据统计方法的建模灵活性。特别是，我们将开发方法，通过不确定性的量化、假设的检验和混杂因素的调节来为结构化数据提供统计推断，并通过混合统计和深度学习模型、总体和分布级别的解释以及鲁棒稀疏解释来改进结构化数据的解释。(2)在方法开发和生物医学应用之间建立反馈回路，在开发新方法时考虑数据分析的需求，并从已开发方法对数据的应用中产生生物医学见解。应用包括MRI， fMRI和显微镜图像分析，纵向疾病进展建模，DNA序列分析和遗传关联研究。