FOR 5363: KI-FOR Fusing Deep Learning and Statistics towards Understanding Structured Biomedical Data

FOR 5363：AI-FOR 融合深度学习和统计学以理解结构化生物医学数据

• 批准号: 459422098
• 金额: --
• 依托单位: Institut für Biologie
• 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/459422098?language=en
• 关键词: 5363; KI; Fusing; Deep; Learning

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（例如图像，基因组序列或时间序列）等生物医学科学的高通量测量构成结构化数据，这些数据的特征是它们的固有依赖性（通常是非矢量性质和存在混杂的影响和采样偏见）的固有依赖性。例如，人口结构，系统的测量工件，非独立抽样或不同的群体年龄分布可能会导致伪造的结果。由于能够捕获输入和输出之间和输出之间的复杂依赖关系，因此在许多应用程序中，深度学习在许多应用程序中都表现出色，从而可以进行准确的预测。尽管最新的可解释人工智能和贝叶斯神经网络取得了进步，但深度学习在评估不确定性，可解释性和验证方面仍然存在局限性。但是，这些是重要的组成部分，以超越预测，而不是理解潜在的生物学。为此，由于可解释的模型输出和统计推断，统计数据传统上已用于生物医学科学中，该统计信息是I.A.提供不确定性的量化，对具有统计误差控制的假设进行混淆和测试的校正。然而，来自经典统计的方法在其建模方面对结构化数据的灵活性以及它们以数据驱动方式捕获复杂的非线性的能力有局限性。在这项研究单元中，我们将机器学习和统计数据中的专家汇集在一起​​，并在生物医学应用中汇集了良好的记录，以解决生物医学应用中的记录，以解决以下总体目标，以提高范围的范围，以提高研究和统计的范围，并将其整合到范围内，并确定范围，并确定范围的范围，并统计范围，并确定统计的范围，并确定统计的范围，并确定统计的范围，并确定统计的范围，并确定了统计学的范围，并确定了统计学的范围，并确定了统计学的范围，并确定了统计的范围，并确定了统计学的范围，并确定了统计的范围。结构化数据的统计方法。特别是，我们将通过量化不确定性，对假设的测试和对混杂因素的调节进行统计推断的方法，并通过混合统计和深度学习模型，人群和分布级别的解释以及在这种方法之间进行研究，从而在较稀疏的解释中进行研究，从而改善了结构化数据的解释。在开发新方法并从开发方法的应用中生成生物医学见解时。应用包括MRI，fMRI和显微镜图像的分析，纵向疾病进展建模，DNA序列分析和遗传关联研究。