Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

新技术提供了获取个人密集“数据云”的能力。然而，在这方面， 这些数据（基因组，转录组， 临床变量等）提出了新的挑战：如何查询如此密集的数据云， 混合数据作为集成集（而不是逐个变量）， 基础，并将联合分子信息转化为临床领域？当前词汇映射 暴力数据挖掘试图使异构数据可互操作和可访问 但它们的产出是零散的，需要专门知识才能汇集成连贯的可操作的 信息.我们提出了DeepTranslate，这是一种创新的方法， 作为发病机制的底物的生物实体的实际物理组织为（i） 网络（数据图）和（ii）跨越多尺度空间的概念层次， 分子到临床。沿着这样的自然结构组织数据源将允许 将新兴的高维数据集转化为临床医生熟悉的概念， 机械的关系。DeepTranslate将采用混合方法来学习和组织其 （i）现有的一般性综合知识来源（政府间组织、环境、地理和信息中心等）的内容 以及（ii）来自两个示范项目的新测量的个体数据云实例：（1） ISB的Pioneer 100和（2）St. Jude终身癌症幸存者。我们将重点关注糖尿病， 测试案例。这两项研究涵盖了一个深层次的生物尺度空间，因此可以测试整个范围 DeepTranslate在特定应用中的多尺度能力。 1.启用的研究问题类型。一个临床医生怎么能发现 在患者的数据云中观察到的“超出范围”的变量， 从基因到临床变量高维的 为每个个体测量100多个不同类型的数据点的研究数据 （“个人数据云”）作为一个集合以集成的方式进行分析（而不是可变的 （按变量）对照现有知识库，并用于改进数据库？ DeepTranslate解决了这两种类型的问题，从而将加速翻译。 未来的个人数据云（A）护理决策和（B）关于新疾病机制的假设 /治疗，从而使供应商和研究人员受益。 2.专业知识和资源的使用。■ ISB：个性化、大数据驱动的先锋 医学（演示项目1）;生物医学内容专业知识;多尺度组学和分子发病机制， 大数据分析，公共访问数据库的住房;查询引擎设计，GUI。 ■ UCSD：生物医学数据集成的领导者;分子和临床自动组装 数据到层次结构;数据类型之间的转换■ U蒙特利尔：生物医学数据库 从文献中筛选和构建基因/蛋白质/药物相互作用网络;机器 学习，开放资源数据库■圣犹达CRH：癌症监测演示项目2， 癌症患者数据分析。 3.潜在的数据和基础设施挑战。(a)现有综合 临床数据源不统一，也没有明确基于生物网络;交叉映射 是在NLM基于词汇关系进行：HPO（表型）与。 SNOMED CT（用于EMR）与IDC或Merck手册（用于疾病）。仔细挑选这些 需要与NLM密切合作。(b)现有的分子途径数据库 是静态的，基于异质非分层人口的平均值，而新的 测量的高维数据云由于个体内的时间波动而变化 和个体间差异。这将如何影响我们混合方法中的个体类型构建， 而要提供统计能力，需要多大的数据云队列还有待确定。 我们的两个示范项目及其独特的深度（高维和多尺度）数据 因此，在规模有限但不断增长的群体中， 在翻译社区学习。

New technologies afford the acquisition of dense “data clouds” of individual humans. However, heterogeneity, dimensionality and multi-scale nature of such data (genomes, transcriptomes, clinical variables, etc.) pose a new challenge: How can one query such dense data clouds of mixed data as an integrated set (as opposed to variable by variable) against multiple knowledge bases, and translate the joint molecular information into the clinical realm? Current lexical mapping and brute-force data mining seek to make heterogeneous data interoperable and accessible but their output is fragmented and requires expertise to assemble into coherent actionable information. We propose DeepTranslate, an innovative approach that incorporates the known actual physical organization of biological entities that are the substrate of pathogenesis into (i) networks (data graphs) and (ii) hierarchies of concepts that span the multiscale space from molecule to clinic. Organizing data sources along such natural structures will allow translation of burgeoning high-dimensional data sets into concepts familiar to clinicians, while capturing mechanistic relationships. DeepTranslate will take a hybrid approach to learn and organize its content from both (i) existing generic comprehensive knowledge sources (GO, KEGG, IDC, etc.) and (ii) newly measured instances of individual data clouds from two demonstration projects: (1) ISB’s Pioneer 100 and (2) St. Jude Lifetime cancer survivors. We will focus on diabetes as test case. These two studies cover a deep biological scale-space and thus can test the full extent of the multiscale capacity of DeepTranslate in a focused application. 1. TYPES OF RESEARCH QUESTION ENABLED. How can a clinician find out that the dozens of “out of range” variables observed in a patient’s data cloud, form a connected set with respect to pathophysiology pathways, from gene to clinical variable? How can the high-dimensional data of studies that measure for each individual 100+ data points of various types (“personal data clouds”) be analyzed as one set in an integrated fashion (as opposed to variable by variable) against existing knowledge bases and also be used to improve the databases? DeepTranslate addresses these two types of questions and thereby will accelerate translation of future personal data clouds into (A) care decisions and (B) hypotheses on new disease mechanisms / treatments, thereby benefiting providers as well as researchers. 2. USE OF EXPERTISE AND RESOURCES. ■ ISB: pioneer in personalized, big-data driven medicine (Demo Project 1); biomedical content expertise; multiscale omics and molecular pathogenesis, big data analysis, housing of databases for public access; query engine designs, GUI. ■ UCSD: leader in biomedical data integration; automated assembly of molecular and clinical data into hierarchical structures; translation between data types ■ U Montreal: biomedical database curation from literature and construction of gene/protein/drug interaction networks; machine learning, open resource database ■ St Jude CRH: Cancer monitoring Demo Project 2, cancer patient data analytics. 3. POTENTIAL DATA AND INFRASTRUCTURE CHALLENGES. (a) Existing comprehensive clinical data sources are not uniform and not explicitly based on biological networks; cross-mapping is being performed at NLM based on lexical relationships: HPO (phenotypes) vs. SNOMED CT (for EMR) vs. IDC or Merck Manual (for diseases). Careful selection of these sources in close collaboration with NLM is needed. (b) Existing molecular pathway databases are static, based on averages of heterogeneous non-stratified populations, while the newly measured high-dimensional data clouds are varied due to intra-individual temporal fluctuation and inter-individual variation. How this will affect building of ontotypes in our hybrid approach, and how large cohorts of data clouds must be to offer statistical power is yet to be determined. Our two Demonstration Projects with their uniquely deep (high-dimensional and multiscale) data in cohorts of limited but growing size are thus crucial first steps in a long journey of collective learning in the TRANSLATOR community.