Developing novel algorithms for spatial molecular profiling technologies
开发空间分子分析技术的新算法
基本信息
- 批准号:10457848
- 负责人:
- 金额:$ 35.65万
- 依托单位:
- 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:2021-08-01 至 2025-04-30
- 项目状态:未结题
- 来源:
- 关键词:Algorithmic SoftwareAlgorithmsAttentionBayesian ModelingBiologicalBiological ProcessCell physiologyCellsCharacteristicsClinicalCommunitiesComplexComputational algorithmComputing MethodologiesDataData AnalysesData SetDevelopmentDiseaseEmbryonic DevelopmentFeasibility StudiesGene ExpressionGenesGenomicsGoalsGraphHeterogeneityImageImage AnalysisInfrastructureIntuitionLeadLocationMachine LearningMessenger RNAMethodologyMethodsModelingMolecularMolecular ProfilingMorphologyNetwork-basedPathologicPathway interactionsPatient-Focused OutcomesPatternPhenotypePhysiologicalProteinsResearchResearch PersonnelSpatial DistributionStatistical MethodsStatistical ModelsStructureTechnologyTissue imagingTissuesVariantVisualizationbasebioinformatics toolbiological researchcell typecomplex datacomputerized toolsdata structuredata visualizationdeep learningdeep learning algorithmdisease diagnosisexperiencefeature selectionflexibilitygraph neural networkimprovedinformatics toolinsightmental functionnovelpreservationsoftware developmenttooltumor heterogeneityuser friendly software
项目摘要
Project Summary
The location, timing and abundance of mRNA and proteins within a tissue underlie the basic molecular
mechanisms of cell functions and physiological and pathological developments. For example, the study of
expression of thousands of genes simultaneously at different locations could reveal great insights into embryo
development, the cooperation of molecular and cellular processes for high-order mental functions, and the
molecular basis and clinical impact of intra-tumor heterogeneity. Recent technology breakthroughs in spatial
molecular profiling (SMP), including both imaging-based technologies and sequencing-based technologies, have
enabled the comprehensive molecular characterization of single cells while preserving their spatial and
morphological contexts. Due to the huge potential to deepen our understanding of the molecular mechanisms of
cellular and physiological phenotypes, SMP technologies are rapidly gaining attention and a large amount of
such data will be generated. However, there are only few computational methods developed to analyze such
rich but complex data, and the limitations of computational methods lead to such valuable data being largely
under-used. The overarching goal of this study is to develop computational methods to analyze SMP data to
characterize detailed molecular spatial distributions and associate such information with cellular phenotypes and
physiological phenotypes. The specific aims are as follows: 1. develop novel spatio-statistical methods to
characterize spatial distributions of gene expression; 2. develop computational methods to characterize cellular
spatial organizations and investigate their relationship with molecular spatial distributions and disease status; 3.
develop user-friendly software to facilitate researchers in SMP data analysis and visualization. In order to achieve
this goal, we have assembled a strong team with complementary expertise in single-cell genomics, tissue image
analysis, spatial modelling, machine learning and software development. If implemented successfully, this
platform will greatly facilitate users in understanding molecular and cellular spatial organization in biological
tissues and provide comprehensive insights into the underlying biological processes.
项目总结
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Guanghua Xiao其他文献
Guanghua Xiao的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Guanghua Xiao', 18)}}的其他基金
Developing computational algorithms for histopathological image analysis
开发组织病理学图像分析的计算算法
- 批准号:
10314050 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level
在单细胞水平上分析和建模整个幻灯片图像数据的信息学工具
- 批准号:
10594240 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Developing novel algorithms for spatial molecular profiling technologies
开发空间分子分析技术的新算法
- 批准号:
10197672 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level
在单细胞水平上分析和建模整个幻灯片图像数据的信息学工具
- 批准号:
10681472 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level
在单细胞水平上分析和建模整个幻灯片图像数据的信息学工具
- 批准号:
10304819 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Developing computational algorithms for histopathological image analysis
开发组织病理学图像分析的计算算法
- 批准号:
10552537 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level
在单细胞水平上分析和建模整个幻灯片图像数据的信息学工具
- 批准号:
10677280 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Developing computational algorithms for histopathological image analysis
开发组织病理学图像分析的计算算法
- 批准号:
10097119 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Developing novel algorithms for spatial molecular profiling technologies
开发空间分子分析技术的新算法
- 批准号:
10625500 - 财政年份:2021
- 资助金额:
$ 35.65万 - 项目类别:
Integrative Analysis to Identify Therapeutic Targets for Lung Cancer
综合分析确定肺癌治疗靶点
- 批准号:
8631669 - 财政年份:2013
- 资助金额:
$ 35.65万 - 项目类别:
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 35.65万 - 项目类别:
Continuing Grant