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MultiViewPortal: Towards a Scalable Web Application for Multiview Learning

MultiViewPortal：面向多视图学习的可扩展 Web 应用程序

基本信息

批准号：
10827749
负责人：
Sandra E Safo
金额：
$ 22.2万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-23 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10827749
关键词：
Address Adoption Algorithms Award Behavioral Bioinformatics Biological Biological Markers Biological Process Code Communities Complex Computer software Computers Data Databases Decentralization Development Devices Dimensions Disease Funding Genomics Goals Grant High Performance Computing Human Microbiome Image Intervention Knowledge Language Learning Medicine Memory Methods Modeling Molecular Molecular Analysis Multiomic Data National Institute of General Medical Sciences Patient Care Positioning Attribute Process Production Proteomics Research Research Personnel Research Project Grants Resources Running Services Software Engineering Technology Testing Therapeutic Intervention United States National Institutes of Health cloud based complex data computing resources cost cost effective data integration data portal data visualization data-driven model disorder subtype experience feasibility testing flexibility high dimensionality high throughput analysis improved innovation insight interest machine learning method metabolomics molecular targeted therapies multiple data sources multiple omics novel parent grant patient stratification phenotypic data response statistical and machine learning tool user-friendly web app web services

项目摘要

Abstract Recent technological advances have enabled the production of vast amounts of diverse but related data (e.g. genomics, metabolomics, proteomics) with rich information that offer remarkable opportunities to understand biological processes involved in complex diseases and to transform medicine. It is now widely-recognized that the mechanisms that underlie complex diseases are more likely to be unraveled by approaches that go beyond analyzing each type of data separately. Analyzing these multifaceted data to obtain useful information and knowledge is challenging because the data are complex, heterogeneous, and high-dimensional, and require a considerable level of analytical sophistication. Most existing software for data integration that address some of these analytical challenges are on-premises and tend to be decentralized, limiting ability to perform comprehensive integrative analysis from anywhere and on any device. Further, they are based on languages that require substantive knowledge in programming, which limits their wide-spread adoption by the research community. The few existing web applications for data integration have limited capabilities in the types of analyses that can be performed. As a first step towards providing a comprehensive, centralized data integration platform, we have developed a web application in Shiny R and are hosting the application on shinyapp.io. However, the compute and memory limitations of shinyapps.io severely constrain the problem size that can be executed. We propose to explore new cloud technologies to enhance our existing workflows for integrating data from multiple sources. Our web application, MultiviewPortal, will enable high-throughput analyses of molecular, imaging, and phenotypic data; it will make it straightforward for all users to integrate data and it will facilitate users using data from NIH-funded projects that might otherwise be complicated to retrieve, given the complexity of many data portals. Ultimately, our portal has the potential to narrow the gap from raw molecular data to biological insights, offer opportunity to expand the definition of complex diseases, and allow to stratify patients and identify those who might benefit from targeted interventions.

摘要最近的技术进步使得能够产生大量不同但相关的数据（例如，基因组学、代谢组学、蛋白质组学），提供丰富的信息，涉及复杂疾病的生物过程，并改变医学。现在人们普遍认识到，复杂疾病背后的机制更有可能被超越这些机制的方法所揭示。分别分析每种数据。分析这些多方面的数据，以获得有用的信息，知识是具有挑战性的，因为数据是复杂的，异构的，高维的，需要相当复杂的分析大多数现有的数据集成软件都可以解决以下问题：这些分析挑战是内部部署的，往往是分散的，限制了执行全面在任何地方和任何设备上进行集成分析。此外，它们基于需要方案编制方面的实质性知识，这限制了研究界广泛采用。用于数据集成的少数现有Web应用程序在分析类型方面的功能有限，可以执行。作为提供全面、集中的数据集成平台的第一步，我们已经在Shiny R中开发了一个Web应用程序，并将该应用程序托管在shinyapp.io上。但 shinyapps.io的计算和内存限制严重限制了可以执行的问题大小。我们我建议探索新的云技术，以增强我们现有的工作流程，源我们的网络应用程序MultiviewPortal将能够对分子、成像和生物信息进行高通量分析。表型数据;它将使所有用户直接整合数据，并将方便用户使用数据从NIH资助的项目，否则可能是复杂的检索，考虑到许多数据的复杂性门户最终，我们的门户网站有可能缩小从原始分子数据到生物学见解的差距，提供机会扩大复杂疾病的定义，并允许对患者进行分层，他们可能会从有针对性的干预中受益。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies.

DOI：
10.1038/s41598-023-46343-1
发表时间：
2023-11-20
期刊：
Scientific reports
影响因子：
4.6
作者：
通讯作者：

Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity.

DOI：
10.1371/journal.pone.0267047
发表时间：
2022
期刊：
PloS one
影响因子：
3.7
作者：
Lipman D;Safo SE;Chekouo T
通讯作者：
Chekouo T

mvlearnR and Shiny App for multiview learning.

DOI：
10.1093/bioadv/vbae005
发表时间：
2024
期刊：
Bioinformatics advances
影响因子：
0
作者：
通讯作者：

Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status.

DOI：
10.1186/s12864-023-09410-5
发表时间：
2023-06-12
期刊：
BMC genomics
影响因子：
4.4
作者：
通讯作者：

Developing A Baseline Metabolomic Signature Associated with COVID-19 Severity: Insights from Prospective Trials Encompassing 13 U.S. Centers.

DOI：
10.3390/metabo13111107
发表时间：
2023-10-24
期刊：
Metabolites
影响因子：
4.1
作者：
Yang K;Kang Z;Guan W;Lotfi-Emran S;Mayer ZJ;Guerrero CR;Steffen BT;Puskarich MA;Tignanelli CJ;Lusczek E;Safo SE
通讯作者：
Safo SE