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

项目摘要/摘要 美国国立卫生研究院和其他机构正在为高通量基因组学实验提供资金， 数据的数字样本以惊人的速度进入公共领域。这些高质量的数据衡量了 跨越所有实验因素的疾病、药物、细胞系、模型生物体等的组学 和条件。这些数字数据样本的重要性在链接同行评议中得到了进一步说明 展示其科学价值的出版物。然而，数字样本的元数据被记录为免费的 深入下游科学研究所需的无生物絮凝的文本。 深度学习是一种革命性的机器智能范式，允许对算法进行编程 从而消除了明确指定规则或逻辑的需要。鉴于医生/科学家曾经 需要首先理解一个问题以编程计算机来解决它，深度学习算法会进行最佳调整 来解决问题。给出足够的样本数据进行训练，深度学习机器智能 在各种任务上胜过人类。今天，深度学习是最先进的图像表现 分类，最重要的是，对于这项提议，用于自然语言处理。 这项提议是关于工程群组辅助深度学习(RAIDLE)机器智能 快速扩展公共数字样本的数字馆藏。我们将首先使用我们的NIH BD2K资助的搜索标签 分析基因表达总括资源(STARGEO.org)以众包人类开放的注释 数字样品。然后，我们将为基于STARGEO的数字管理开发和训练深度学习算法 在学习相关联的自由文本元数据时，每个数字样本。鉴于生物医学的持续洪流 公共领域的数据，摇篮可能是将数字馆藏扩展到 精准医疗理想。 最后，我们将用两个大的-- 规模和独立的分子数据集：1)癌症基因组图谱(TCGA)，2)加速 药物伙伴关系-阿尔茨海默病(AMP-AD)。我们假设开放样本的摇篮数字管理 将使用大量大数据加强这两个不同的疾病项目，以发现潜在的生物标记物 和基因靶标。因此，这项工作的成功资助和完成可能会大大减轻 通过提高生物医学大数据数字管理的效率和效果，提高患者的疾病风险。

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.