Core C - Biostatistics & Bioinformatics Core
核心 C - 生物统计学
基本信息
- 批准号:10434086
- 负责人:
- 金额:$ 26.44万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-07-19 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:AdjuvantAlgorithmsAmericanAutoantibodiesBioinformaticsBiological MarkersBiometryBiostatistics Shared ResourceCancer CenterCaringClinicalClinical DataClinical TrialsCollaborationsCommunitiesComputational algorithmConsultationsDataData AnalysesData CollectionDevelopmentDoctor of PhilosophyEnsureEpigenetic ProcessFundingGeneticGrantImmunologic MarkersImmunotherapyInformaticsInformation ResourcesInstitutesJointsLaboratoriesLaboratory StudyLeadershipMalignant NeoplasmsMedical centerMedicineMethodsMicroRNAsMissionModelingOutcomePatient CarePatientsPrediction of Response to TherapyPrimary NeoplasmPublicationsRandomized Clinical TrialsReportingResearchResearch DesignResearch PersonnelResearch Project GrantsResourcesSample SizeSamplingSourceStatistical Data InterpretationToxic effectTranslational ResearchTreatment outcomeTreatment-related toxicityTumor TissueWorkXCL1 geneanticancer researchbasebiomarker developmentbiomarker identificationcancer therapycareerdata integrationdata managementdesigndiverse datagenomic datahigh dimensionalityimmune-related adverse eventsinnovationmelanomamembermicrobial hostmicrobiomemultiple data sourcesopen sourcepatient subsetspersonalized managementpersonalized medicinepredictive markerpredictive modelingpredictive signaturesenior facultysequencing platformtranscriptome sequencingtranslational impacttranslational studytreatment response
项目摘要
PROJECT SUMMARY
The Biostatistics and Bioinformatics Core (Core C) of the NYU Melanoma SPORE will provide statistical and
bioinformatics collaboration and consultation to all SPORE Research Projects and Cores. Consultation is
available from the study design and planning stages through implementation, data management, statistical and
bioinformatics analysis, and interpretation of results. Core C will provide support for all proposed laboratory
studies and translational studies, including biomarker development based on samples from existing and new
clinical trials to support the overarching mission and central scientific strategy of the NYU Melanoma SPORE.
Furthermore, strategies for the systematic selection of samples from all the projects and the coordination of
informatics support in Core C will permit the overall integration of results across projects to develop
comprehensive models to predict treatment outcomes and toxicity. Core C draws on and integrates an extensive
fund of knowledge, resources, and expertise across the NYU Langone Medical Center (NYULMC) and NYU
Perlmutter Cancer Center (PCC) to serve the NYU Melanoma SPORE. Co-Director Dr. Yongzhao Shao is
Deputy Director of the PCC Biostatistics Shared Resource (BSR) and Dr. Itai Yanai is the Director of the Institute
for Computational Medicine, respectively, and will provide integrated biostatistical and bioinformatics support
and ensure maximum utilization of all institutional resources and facilities. This will empower the provision of
expertise in all aspects of statistical design; power/sample size calculations; systematic sample selection
strategies for efficient data integration and analyses; and integration of data from multiple sources including
laboratory data, clinical data, and data from diverse sequencing platforms. Core C will develop innovative
statistical and bioinformatics methods, including scalable computation algorithms, for identifying and evaluating
biomarkers in translational studies, and will make these newly developed algorithms publicly available to the
larger cancer research community. In particular, Core C’s identification of biomarkers that may optimize the
personalized management of advanced melanoma patients will enable the development of integrated,
multivariable predictive models for treatment response and toxicity. This work, based on biomarkers discovered
across SPORE Projects, will contribute to personalized melanoma management and amplify the translational
impact of the NYU Melanoma SPORE.
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 }}
Yongzhao Shao其他文献
Yongzhao Shao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('Yongzhao Shao', 18)}}的其他基金
相似海外基金
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:
$ 26.44万 - 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:
$ 26.44万 - 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:
$ 26.44万 - 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:
$ 26.44万 - 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:
$ 26.44万 - 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:
$ 26.44万 - 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:
$ 26.44万 - 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:
$ 26.44万 - 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:
$ 26.44万 - 项目类别:
Continuing Grant
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:
$ 26.44万 - 项目类别:
Research Grant














{{item.name}}会员




