Cloud-Based Machine Learning and Biomarker Visual Analytics for Salivary Proteomics

标题：基于云的机器学习和唾液蛋白质组的BiomMarker可视化分析 项目总结 云计算和开放获取数据平台提供基本和关键的生物信息学资源 用于数据分析和知识发现，以支持数据密集型和数据驱动的研究，例如 蛋白质组学。这项补充提案的家长拨款为DE016937，“A Foundation for the Oral” 微生物组和超基因组组“的目标是提供精选的组学资源，以帮助科学 社区了解口腔中微生物-微生物和微生物-宿主的相互作用如何影响 人类的健康和疾病。唾液是一种具有复杂成分和功能的生物流体，具有 使医学界能够广泛诊断和治疗疾病的潜力。这是一项旨在 测试基于云的资源的效用，以提高对唾液及其相关的大量数据的了解 蛋白质组数据集。这些信息将通过人类唾液蛋白质组整合到父母的资助中 Wiki-HSP Wiki-开发用于收集和管理来自人类的蛋白质组信息 唾液。目前，该数据库允许口腔和生物医学研究社区的成员 探索各种蛋白质组数据集，使用有限的可视化工具来研究唾液成分、相互作用、 和初始聚类。虽然我们的HSP Wiki管道允许用户通过调查健康状况来询问数据 和疾病模式，唾液生物标记物的发现存在差距，包括缺乏视觉分析 研究蛋白质-蛋白质相互作用的工具和工具。此外，最尖端的机器学习(ML) 方法还没有被应用于蛋白质组数据分析，包括唾液蛋白质组的分析。目标是 这个概念验证项目的目的是开发基于云的工具，以增强当前的HSP Wiki可视化 分析，实施ML并改进我们对唾液衍生蛋白的解释，以实现更高效 和可靠的唾液生物标记物的发现。有了这个目的，第一个目标就是开发基于云的 可视化分析平台，支持唾液蛋白质组生物标志物的机器学习识别。我们 将测试和量化并行计算和机器学习如何有效和高效地处理 与传统桌面相比，用于发现新生物标记物的大型唾液蛋白质组数据集 工具。第二个目标是开发一种可扩展的按需云管道来实现蛋白质 使用AlphaFold2的预测，以及使用Molstar的3D模型比较工具。预期的结果 将为研究社区提供增强的工具和在线资源，以更好地发现 健康和疾病以及宿主和微生物蛋白的生物标记物。我们将对效率进行量化 这些工具及其在产生蛋白质结构新发现方面的有效性。基于云的 界面、可视化分析工具、信息管道和并行计算将广泛传播 促进发现，提高蛋白质组研究和翻译的严密性和透明度 唾液生物标志物的应用。

Title: Cloud-Based Machine Learning and Biomarker Visual Analytics for Salivary Proteomics PROJECT SUMMARY Cloud computing and open-access data platforms provide essential and critical bioinformatics resources for data analysis and knowledge discovery to support data-intensive and data-driven research such as proteomics. The parent grant for this supplement proposal, DE016937, “A Foundation for the Oral Microbiome and Metagenome,” has the goal of providing curated ‘omic resources to help the scientific community understand how microbe-microbe and microbe-host interactions in the oral cavity affect human health and disease. Saliva is a biofluid with complex composition and function that has the potential to allow the medical community broadly to diagnose and treat disease. This is a proposal to test the utility of cloud-based resources to improve understanding of saliva and its associated large proteomic datasets. This information will integrate into the parent grant via the Human Salivary Proteome Wiki - HSP Wiki – which was developed to aggregate and curate proteome information from human saliva. Currently, this database allows members of the oral and biomedical research communities to explore various proteomic datasets, with limited visualization tools for salivary composition, interactions, and initial clustering. While our HSP Wiki pipeline allows users to interrogate the data by surveying health and disease patterns, there are gaps in salivary biomarker discovery, including lack of visual analytic tools and tools to study protein-protein interactions. In addition, most cutting-edge machine learning (ML) methods have not been applied to proteomic data analysis, including of the salivary proteome. The goal of this proof-of-concept project is to develop cloud-based tools to enhance the current HSP Wiki visual analytics, implement ML and improve our interpretation of salivary derived proteins towards more efficient and reliable salivary biomarker discovery. With this purpose, the first aim is to develop a cloud-based visual analytics platform to support machine learning identification of salivary proteomic biomarkers. We will test and quantify how parallel computing and machine learning can effectively and efficiently process large salivary proteomic datasets for novel biomarker discovery when compared to conventional desktop tools. The second aim is to develop a scalable and on-demand cloud pipeline to implement protein prediction using AlphaFold2, and a comparison tool for 3D models using MolStar. The expected outcome will be to provide the research community with enhanced tools and online resources to better discover biomarkers in health and disease and for both host and microbial proteins. We will quantify the efficiency of these tools and their effectiveness in generating new discoveries of protein structure. The cloud-based interface, visual analytical tools, informatic pipelines, and parallel computing will be broadly disseminated to catalyze discovery, enhancing rigor and transparency in proteomic research and translation applications to salivary biomarkers.