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

基于云的机器学习和唾液蛋白质组生物标志物可视化分析

• 批准号: 10827649
• 负责人: Floyd E Dewhirst
• 金额: $ 31.76万
• 依托单位: ADA FORSYTH INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10827649
• 关键词: 3-Dimensional; Affect; Artificial Intelligence; Binding; Bioinformatics; Biological Markers; Biomedical Research; Cloud Computing; Code; Communities; Complex; Data; Data Analyses; Data Analytics; Data Set; Databases; Deglutition; Diagnosis; Digestion; Disease; Effectiveness; Environment; Evaluation; Foundations; Funding; Galaxy; Goals; Health; Health Surveys; Homeostasis; Human; Informatics; Investigation; Knowledge Discovery; Learning; Libraries; Liquid substance; Machine Learning; Manuals; Mastication; Medical; Methods; Microbe; National Institute of Dental and Craniofacial Research; Oral; Oral cavity; Outcome; Pattern; Physiological; Play; Post-Translational Protein Processing; Process; Proteins; Proteome; Proteomics; Pythons; Reproducibility; Research; Resources; Role; Saliva; Salivary; Salivary Proteins; Sampling; Site; Speed; Standardization; Structure; Taste Perception; Technology; Testing; Time; Viral Proteins; Visual; Visualization; Visualization software; analytical method; analytical tool; bioinformatics resource; biomarker discovery; biomarker identification; cloud based; computing resources; data access; host-microbe interactions; improved; insight; machine learning method; member; metagenome; microbial; novel; novel marker; online resource; oral microbiome; parallel computer; parent grant; pathogen; programs; protein biomarkers; protein function; protein protein interaction; protein structure; protein structure prediction; statistics; three dimensional structure; three-dimensional modeling; tool; translational applications; web portal; wiki

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.

标题：基于云的机器学习和生物标志物可视化分析唾液蛋白质组学 项目摘要 云计算和开放获取数据平台提供了重要的生物信息学资源 用于数据分析和知识发现，以支持数据密集型和数据驱动型研究， 蛋白质组学这项补充提案的母基金，DE 016937，“口头教育基金会”， 微生物组和宏基因组”的目标是提供精选的组学资源，以帮助科学家 社区了解口腔中微生物与微生物和微生物与宿主的相互作用如何影响 人类健康和疾病。唾液是一种具有复杂组成和功能的生物流体， 这将使医学界能够广泛地诊断和治疗疾病。这是一个建议， 测试基于云的资源的实用性，以提高对唾液及其相关的大型 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补助金中 Wiki - HSP Wiki -开发用于聚合和管理来自人类的蛋白质组信息 口水目前，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进行唾液组成，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，唾液生物标志物的发现存在差距，包括缺乏视觉分析 研究蛋白质相互作用的工具和工具。此外，最前沿的机器学习（ML） 方法还没有应用于蛋白质组数据分析，包括唾液蛋白质组。目标 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视觉 分析，实施ML并改进我们对唾液衍生蛋白的解释， 和可靠的唾液生物标志物发现。有了这个目的，第一个目标是开发一个基于云的 可视化分析平台，以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，用于新生物标志物发现的大型唾液蛋白质组数据集 工具.第二个目标是开发一个可扩展和按需的云管道来实现蛋白质 使用AlphaFold 2进行预测，以及使用MolStar进行3D模型的比较工具。预期结果 将为研究界提供增强的工具和在线资源，以更好地发现 生物标志物在健康和疾病以及宿主和微生物蛋白质。我们将量化效率 这些工具及其在蛋白质结构新发现中的有效性。基于云的 界面、可视化分析工具、信息管道和并行计算将得到广泛传播 促进发现，提高蛋白质组学研究和翻译的严谨性和透明度 唾液生物标志物的应用。