Crowd-Assisted Deep Learning (CrADLe) Digital Curation to Translate Big Data into Precision Medicine

群体辅助深度学习 (CrADLe) 数字管理将大数据转化为精准医学

• 批准号: 10063300
• 负责人: Dexter D Hadley
• 金额: $ 37.58万
• 依托单位: UNIVERSITY OF CENTRAL FLORIDA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10063300
• 关键词: Crowd; Assisted; Deep; Learning; CrADLe

PROJECT SUMMARY/ABSTRACT The NIH and other agencies are funding high-throughput genomics (‘omics) experiments that deposit digital samples of data into the public domain at breakneck speeds. This high-quality data measures the ‘omics of diseases, drugs, cell lines, model organisms, etc. across the complete gamut of experimental factors and conditions. The importance of these digital samples of data is further illustrated in linked peer-reviewed publications that demonstrate its scientific value. However, meta-data for digital samples is recorded as free text without biocuration necessary for in-depth downstream scientific inquiry. Deep learning is revolutionary machine intelligence paradigm that allows for an algorithm to program itself thereby removing the need to explicitly specify rules or logic. Whereas physicians / scientists once needed to first understand a problem to program computers to solve it, deep learning algorithms optimally tune themselves to solve problems. Given enough example data to train on, deep learning machine intelligence outperform humans on a variety of tasks. Today, deep learning is state-of-the-art performance for image classification, and, most importantly for this proposal, for natural language processing. This proposal is about engineering Crowd Assisted Deep Learning (CrADLe) machine intelligence to rapidly scale the digital curation of public digital samples. We will first use our NIH BD2K-funded Search Tag Analyze Resource for Gene Expression Omnibus (STARGEO.org) to crowd-source human annotation of open digital samples. We will then develop and train deep learning algorithms for STARGEO digital curation based on learning the associated free text meta-data each digital sample. Given the ongoing deluge of biomedical data in the public domain, CrADLe may perhaps be the only way to scale the digital curation towards a precision medicine ideal. Finally, we will demonstrate the biological utility to leverage CrADLe for digital curation with two large- scale and independent molecular datasets in: 1) The Cancer Genome Atlas (TCGA), and 2) The Accelerating Medicines Partnership-Alzheimer’s Disease (AMP-AD). We posit that CrADLe digital curation of open samples will augment these two distinct disease projects with a host big data to fuel the discovery of potential biomarker and gene targets. Therefore, successful funding and completion of this work may greatly reduce the burden of disease on patients by enhancing the efficiency and effectiveness of digital curation for biomedical big data.

项目概要/摘要 美国国立卫生研究院 (NIH) 和其他机构正在资助高通量基因组学（“组学”）实验，这些实验将 数据的数字样本以极快的速度进入公共领域。这些高质量的数据衡量了 “涵盖所有实验因素的疾病、药物、细胞系、模式生物等组学 和条件。这些数字数据样本的重要性在链接的同行评审中得到进一步说明 证明其科学价值的出版物。然而，数字样本的元数据是免费记录的 没有深入下游科学探究所需的生物管理的文本。 深度学习是革命性的机器智能范例，允许算法进行编程 从而消除了显式指定规则或逻辑的需要。而医生/科学家曾经 需要首先理解问题才能对计算机进行编程来解决它，深度学习算法可以优化调整 自己去解决问题。给定足够的示例数据来训练深度学习机器智能 在各种任务上都超越人类。如今，深度学习是图像处理领域最先进的表现 分类，对于本​​提案来说最重要的是自然语言处理。 该提案是关于设计人群辅助深度学习（CrADLe）机器智能以 快速扩展公共数字样本的数字管理。我们将首先使用 NIH BD2K 资助的搜索标签 分析基因表达综合资源 (STARGEO.org) 以众包人类注释开放 数字样本。然后，我们将为基于 STARGEO 的数字策展开发和训练深度学习算法 学习每个数字样本相关的自由文本元数据。鉴于生物医学的持续泛滥 CrADLe 可能是将数字管理扩展到公共领域的唯一方法 精准医疗的理想选择。 最后，我们将展示利用 CrADLe 进行数字管理的生物学效用，其中包括两个大型项目： 规模和独立的分子数据集：1) 癌症基因组图谱 (TCGA)，以及 2) 加速 药物合作伙伴-阿尔茨海默病 (AMP-AD)。我们假设 CrADLe 开放样本的数字管理 将利用主机大数据来增强这两个不同的疾病项目，以推动潜在生物标志物的发现 和基因靶标。因此，成功资助并完成这项工作可能会大大减轻 通过提高生物医学大数据数字治疗的效率和有效性来治疗患者的疾病。