Collaborative Research: BeeHive: A Cross-Problem Benchmarking Framework for Network Biology
合作研究:BeeHive:网络生物学的跨问题基准框架
基本信息
- 批准号:2233969
- 负责人:
- 金额:$ 9.25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-05-01 至 2026-04-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Many important aspects of biology involve relationships between the molecules within cells. For example, a medicine may turn off a diseased protein, or protein may activate an important gene. These individual relationships organize into larger biological networks. Many computational methods aim to predict these types of network relationships and which relationships control essential biological processes. This project will establish a computational framework called BeeHive to support running and comparing modern computational tools for studying biological networks. BeeHive will make it considerably easier to analyze biological data with these methods and evaluate their strengths and weaknesses. The framework will automatically update a website that tests top methods on a variety of biological use cases, which will provide important benchmarking and assessments for the network biology scientific community. The project will showcase BeeHive with biological applications in gene regulation, protein signaling, and chemical target networks. BeeHive will be used in undergraduate research experiences through a Summer Research Institute across the three project sites. The project will develop BeeHive, a general platform for multiple types of network biology workflows. BeeHive will provide a shared framework and modular components that implement common elements of network biology analyses including installation of algorithms, data pre-processing, cross-validation methods, and network visualization. The BeeHive infrastructure will enable running many network algorithms at scale from a single interface. This strategy will support rigorous benchmarking of network algorithms and greatly simplify testing multiple algorithms on a new biological dataset. The project will apply BeeHive to three representative applications, namely gene regulatory network inference, pathway reconstruction, and small molecule-protein target prediction applications. These problems are important in the genomics and bioinformatics research communities due to recent computational and biotechnological advances, such as graph neural networks and single-cell RNA-sequencing. Key components of BeeHive will include a modular and general purpose Python package that can be reused, a template Snakemake workflow to execute the shared steps of network biology analysis from data pre-processing through network visualization, a framework for continuous benchmarking that uses concepts from continuous integration in software engineering, Docker containers for tens of existing network biology algorithms, and datasets spanning yeast, mouse, human, and plants. Core objectives of BeeHive include advancing computational infrastructure for network biology analysis and benchmarking as well as creating an active and growing scientific community to create rigorous and standardized benchmarking frameworks and contribute methods and datasets to BeeHive. In the long term, the project will broadly generalize to other aspects of network biology and can catalyze analogous efforts in other domains in systems and computational biology. This project will train graduate students and create a Summer Research Institute that hosts six undergraduate researchers per year across the three project sites. Recruitment for the Summer Research Institute will emphasize broadening participation of students from historically marginalized groups. Results from this project will be available at https://bioinformatics.cs.vt.edu/~murali/beehive.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
生物学的许多重要方面涉及细胞内分子之间的关系。例如,一种药物可以关闭一种患病的蛋白质,或者蛋白质可以激活一种重要的基因。这些个体关系组成了更大的生物网络。许多计算方法旨在预测这些类型的网络关系,以及哪些关系控制着基本的生物过程。该项目将建立一个名为BeeHive的计算框架,以支持运行和比较用于研究生物网络的现代计算工具。BeeHive将使使用这些方法分析生物数据并评估其优缺点变得更加容易。该框架将自动更新一个网站,测试各种生物用例的顶级方法,这将为网络生物学科学界提供重要的基准和评估。该项目将展示BeeHive在基因调控、蛋白质信号传导和化学靶点网络方面的生物应用。BeeHive将通过三个项目地点的夏季研究所用于本科生研究经验。该项目将开发BeeHive,这是一个用于多种类型网络生物学工作流程的通用平台。BeeHive将提供一个共享的框架和模块化组件,用于实现网络生物学分析的常见元素,包括算法安装、数据预处理、交叉验证方法和网络可视化。BeeHive基础设施将使许多网络算法能够从单个接口大规模运行。该策略将支持对网络算法进行严格的基准测试,并大大简化在新的生物数据集上测试多种算法。该项目将把BeeHive应用于三个具有代表性的应用,即基因调控网络推理、通路重建和小分子蛋白靶点预测应用。这些问题是重要的基因组学和生物信息学研究社区,由于最近的计算和生物技术的进步,如图形神经网络和单细胞RNA测序。BeeHive的关键组件将包括一个可重复使用的模块化和通用Python包,一个模板Snakemake工作流,用于执行从数据预处理到网络可视化的网络生物学分析的共享步骤,一个用于持续基准测试的框架,该框架使用软件工程中的持续集成概念,用于数十种现有网络生物学算法的Docker容器,以及跨越酵母,小鼠,人类,和植物。BeeHive的核心目标包括推进网络生物学分析和基准测试的计算基础设施,以及创建一个活跃和不断增长的科学社区,以创建严格和标准化的基准测试框架,并为BeeHive提供方法和数据集。从长远来看,该项目将广泛推广到网络生物学的其他方面,并可以促进系统和计算生物学其他领域的类似努力。该项目将培训研究生,并创建一个夏季研究所,每年在三个项目地点接待六名本科生研究人员。夏季研究所的招聘将强调扩大历史上被边缘化群体的学生的参与。该项目的结果将在www.example.com上公布https://bioinformatics.cs.vt.edu/~murali/beehive.This奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Anna Ritz其他文献
Finite-temperature properties of string-net models
弦网模型的有限温度特性
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Anna Ritz;Jean;Steven H. Simon;Julien Vidal - 通讯作者:
Julien Vidal
Effective models for dense vortex lattices in the Kitaev honeycomb model
Kitaev 蜂窝模型中密集涡晶格的有效模型
- DOI:
10.1103/physrevb.109.115107 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
David J. Alspaugh;Jean;Anna Ritz;Julien Vidal - 通讯作者:
Julien Vidal
Posttraumatic stress disorder symptomology as measured by PCL-5 and its relationships to resilience, hostility and stress among paramedics and social professionals.
通过 PCL-5 测量的创伤后应激障碍症状及其与护理人员和社会专业人员的复原力、敌意和压力的关系。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:4.1
- 作者:
Anna Alexandrov;Nóra Román;Petra Kovács;Anna Ritz;Mónika Kissné Viszket;Zsuzsa Kaló - 通讯作者:
Zsuzsa Kaló
Wegner-Wilson loops in string nets
弦网中的韦格纳-威尔逊环
- DOI:
10.1103/physrevb.103.075128 - 发表时间:
2020 - 期刊:
- 影响因子:3.7
- 作者:
Anna Ritz;J. Fuchs;J. Vidal - 通讯作者:
J. Vidal
Anna Ritz的其他文献
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{{ truncateString('Anna Ritz', 18)}}的其他基金
NSF Student Travel Grant for the 2022 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)
NSF 学生旅费资助 2022 年计算网络生物学国际研讨会:建模、分析和控制 (CNB-MAC)
- 批准号:
2230929 - 财政年份:2022
- 资助金额:
$ 9.25万 - 项目类别:
Standard Grant
CAREER: Network-Based Signaling Pathway Analysis: Methods and Tools for Turning Theory into Practice
职业:基于网络的信号通路分析:将理论转化为实践的方法和工具
- 批准号:
1750981 - 财政年份:2018
- 资助金额:
$ 9.25万 - 项目类别:
Continuing Grant
A Course-Based Undergraduate Conference Experience in Computational Biology
计算生物学课程本科会议经验
- 批准号:
1643361 - 财政年份:2016
- 资助金额:
$ 9.25万 - 项目类别:
Standard Grant
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