Lagrangian computational modeling for biomedical data science

生物医学数据科学的拉格朗日计算模型

• 批准号: 10307595
• 负责人: Gustavo Kunde Rohde
• 金额: $ 36.02万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2023-09-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10307595
• 关键词: 3-Dimensional; Accountability; Address; Algorithmic Analysis; Area; Biological; Biological Models; Biology; Biophysics; Brain; Cancer Detection; Cartilage; Cell model; Cells; Classification; Collaborations; Communication; Communities; Computer Models; Computer software; Data; Data Analyses; Data Reporting; Data Science; Data Scientist; Data Set; Development; Disease; Drug Screening; Engineering; Flow Cytometry; Fluorescence; Gene Expression; Generations; Goals; Heart; Image; Knee; Laboratories; Learning; Letters; Libraries; Link; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Neoplasms; Mass Spectrum Analysis; Mathematics; Measurement; Medical Imaging; Methodology; Modeling; Molecular; Morphology; Optics; Organ; Performance; Plant Roots; Population; Pythons; Research; Scientist; Signal Transduction; System; Techniques; Technology; Training; Universities; Virginia; Visualization; absorption; algorithm development; artificial neural network; base; biomedical data science; biophysical properties; brain morphology; cellular imaging; clinical application; clinical practice; convolutional neural network; cost; data space; deep learning; deep learning model; deep neural network; effectiveness testing; electric impedance; experimental study; graphical user interface; gray matter; heart imaging; image reconstruction; imaging science; learning strategy; machine learning model; mathematical algorithm; mathematical model; mathematical theory; microscopic imaging; models and simulation; neural network; patient stratification; patient subsets; predictive modeling; radiological imaging; technology research and development; tool; voltage

The goal of the project is to develop a new mathematical and computational modeling framework for from biomedical data extracted from biomedical experiments such as voltages, spectra (e.g. mass, magnetic resonance, impedance, optical absorption, …), microscopy or radiology images, gene expression, and many others. Scientists who are looking to understand relationships between different molecular and cellular measurements are often faced with questions involving deciphering differences between different cell or organ measurements. Current approaches (e.g. feature engineering and classification, end-to-end neural networks) are often viewed as “black boxes,” given their lack of connection to any biological mechanistic effects. The approach we propose builds from the “ground up” an entirely new modeling framework build based on recently developed invertible transformation. As such, it allows for any machine learning model to be represented in original data space, allowing for not only increased accuracy in prediction, but also direct visualization and interpretation. Preliminary data including drug screening, modeling morphological changes in cancer, cardiac image reconstruction, modeling subcellular organization, and others are discussed.

该项目的目标是开发一种新的数学和计算 从生物医学数据中提取的建模框架 例如电压、光谱（例如质量，磁共振， 阻抗、光吸收、.）、显微镜或放射学图像、基因 表情，还有很多。科学家们希望了解 不同分子和细胞测量之间的关系通常 面临的问题涉及破译不同细胞之间的差异， 器官测量当前的方法（例如，特征工程和 分类，端到端神经网络）通常被视为“黑匣子”， 因为它们与任何生物机械效应都没有联系。的方法 我们建议从头开始构建一个全新的建模框架， 基于最近开发的可逆变换构建。因此，它允许 在原始数据空间中表示的任何机器学习模型，允许 不仅提高了预测的准确性，而且直接可视化， 解释。初步数据包括药物筛选、形态学建模 肿瘤变化，心脏图像重建，亚细胞建模 组织和其他人进行了讨论。