Biomarkers of Cerebral Cavernous Angioma with Symptomatic Hemorrhage (CASH) - Supplemental

总结 脑海绵状血管瘤（CA）是一种毛细血管微血管病，影响超过一百万美国人， 使他们有过早脑出血的危险只有不到20万人患有 近期症状性出血（CASH）最有可能再次出血，并伴有严重的临床后遗症， 试验准备和治疗开发的主要焦点。候选生物标志物正在出现， 正在进行的机制和差异转录组研究，这提高了诊断和预测， 现金，影响临床决策，并帮助分层临床试验中的高风险病例。一个正在进行的项目 （R 01 NS 114552）已召集领先的临床CA研究人员部署先进的计算生物学 方法，包括监督机器学习（ML），以发现和验证新的候选生物标志物。 它旨在确定组合生物标志物贡献的最佳聚类和加权，以实现最佳诊断 预测的准确性。最初旨在结合候选蛋白质和microRNA的水平，最近 这些发现迫使将循环代谢物与机械联系包括在内， 在发现队列中具有很强的诊断和预后相关性。血浆的最佳加权组合 分子将在已经招募的大型验证队列中进行测试，以评估它们在性别，年龄， 相关临床亚组和多个招募中心。该项目测试了一种新的集成方法， 在机械定义的脑血管疾病中开发生物标志物，并具有相关的使用背景。 虽然适用于其他神经系统疾病，但我们的数据用于ML分析的实施及其使用， 其他研究人员的可用性受到当前存储库中共享数据结构不一致的限制。 此外，缺乏数据元素的协调或多组数据元素的直观连接 同样的病人。随着代谢物添加到我们的多组生物标志物中， 候选人为了响应NOSI NOT-OD-23-082，我们组建了一个具有计算专业知识的团队， 生物学，临床生物标志物研究，数据科学和统计学，谁将致力于解决这些问题的解决方案 问题.首先，我们将在结构化存储库中共享每种类型的原始数据，这些数据与每种类型的分析相匹配， 数据类型我们还根据数据结构、标准化和命名的最佳实践设计了数据库 我们将通过Dryad分享的约定，其中将包括为ML和其他AI和Deep准备的数据。 学习实现。我们还建议创建一个GitHub存储库，其中将包含详细的描述 Dryad中的数据和我们研究中用于ML实现的代码。我们将连接不同的 向资料库提交的资料，以方便使用同一主题的一种以上的数据类型。最后， 除了发布该项目的ML多组学数据分析的主要结果外，我们还建议 发表关于连接共享数据结构的方法论文，为未来的ML，人工智能和 其他研究人员的深度学习分析。

SUMMARY Cerebral cavernous angioma (CA) is a capillary microangiopathy affecting more than a million Americans, predisposing them to a premature risk of brain hemorrhage. Fewer than 200,000 cases who have suffered a recent symptomatic hemorrhage (CASH) are most likely to re-bleed again with serious clinical sequelae, and are the primary focus of trial readiness and therapeutic development. Candidate biomarkers are emerging from ongoing mechanistic and differential transcriptome studies, which enhance the diagnosis and prediction of CASH, influence clinical decisions, and help stratify high-risk cases in clinical trials. An ongoing project (R01NS114552) has assembled leading clinical CA researchers to deploy advanced computational biology approaches, including supervised machine learning (ML), to discover and validate novel candidate biomarkers. It aims to determine the best clustering and weighing of combined biomarker contributions for optimal diagnostic and predictive accuracy. Initially aimed at combining levels of candidate proteins and microRNAs, recent discoveries have compelled the inclusion of circulating metabolites with mechanistic links, which demonstrate strong diagnostic and prognostic associations in discovery cohorts. The best weighted combination of plasma molecules will be tested in a large validation cohort already recruited, to assess their relevance in sex, age, relevant clinical subgroups and multiple recruitment sites. The project tests a novel integrational approach of biomarker development in a mechanistically defined cerebrovascular disease with a relevant context of use. While applicable to other neurological diseases, the implementation of our data for ML analyses, and its use and usability by other investigators, are limited by inconsistent structure of shared data in current repositories. Furthermore, there is a lack of harmonization of data elements or intuitive connectivity of multiomic data elements from the same patients. This problem is amplified with the addition of metabolites to our multiomic biomarker candidates. In response to NOSI NOT-OD-23-082, we have assembled a team with expertise in computational biology, clinical biomarker research, data science and statistics, who will work on solutions to address these issues. First, we will share each type of raw data in structured repositories that match each type of assay and data type. We also designed a database under best practices for data structure, standardization, and naming conventions that we will share through Dryad, which will include data that is ready for ML and other AI and Deep learning implementations. We further propose creating a GitHub repository that will include a detailed description of the data in Dryad and the codes used for the ML implementation in our study. We will connect the different submissions to the repositories to facilitate the use of more than one data type from the same subjects. Lastly, in addition to publishing the primary results of ML multiomic data analyses from this project, we propose to publish a methods paper on connected shared data structure for readiness in future ML, artificial intelligence and deep learning analyses by other investigators.