IUCRC Phase I University of Michigan Ann Arbor: Center for Data-Driven Drug Development and Treatment Assessment (DATA)
IUCRC 第一阶段密歇根大学安娜堡分校:数据驱动药物开发和治疗评估中心 (DATA)
基本信息
- 批准号:2209546
- 负责人:
- 金额:$ 75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-08-15 至 2027-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Drug development and assessment present major challenges, in particular: 1) In the early stages of drug discovery, it is costly and time-consuming to screen thousands of possible drugs. 2) When healthcare providers use the drug for treatment, it is highly important to assess their efficacy as well as potential adverse effects. Novel computational approaches, such as machine learning (ML) and artificial intelligence (AI), present solutions not only for drug design but also for personalized treatments. The Center for Data-Driven Drug Development and Treatment Assessment (DATA), through collaboration with its industry partners, intends to promote such computational solutions within precompetitive research.In recent years AI and ML methods have impacted many aspects of society; DATA will develop and apply novel AI/ML methods for several aspects of drug design and assessment such as screening/detecting interactions between drugs and targets, and treatment customization considering preexisting conditions. DATA’s mathematical algorithms and methodologies can result in tools and solutions to address major obstacles throughout the lifecycle of drug design and treatment monitoring. The Center also develops large databases by integrating and curating new/existing public and proprietary healthcare/pharmaceutical datasets, allowing new resources for modeling, development, and assessment of new drugs.DATA will develop programs to educate the next generation of data scientists, especially those from underrepresented groups. DATA will form new collaborations beyond academia to translate data science applications into impactful products/services, thereby reducing drug development costs while optimizing treatments and improving overall patient health. This in turn reduces the financial/societal burden on patients and providers alike. DATA will enable direct interactions between trainees and pharmaceutical/healthcare industry leaders, providing trainees with invaluable insight into real-world needs. DATA will address the urgent need for additional graduates with combined expertise in data science, drug design, and treatment assessment by directly training this future workforce.The data integration, curation and storage processes are intended to continue beyond the scope and duration of the Phase I (and Phase II, if applicable). DATA intends to continue collecting, curating, integrating, and pre-processing databases shared by the current and future members of DATA as well as publicly available databases. A subset of these integrated databases, some of which will be encrypted through our Fully Homomorphic Encryption approach, can be shared with external entities under the guidelines set by the Center’s bylaws. Databases and software tools will be shared by the partners through a dedicated and secure website with documentation.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.
药物开发和评估提出了主要挑战,特别是:1)在药物发现的早期阶段,筛选数千种可能的药物是昂贵和耗时的。2)当医疗保健提供者使用药物进行治疗时,评估其疗效以及潜在的不良反应非常重要。机器学习(ML)和人工智能(AI)等新型计算方法不仅为药物设计提供了解决方案,还为个性化治疗提供了解决方案。数据驱动的药物开发和治疗评估中心(DATA)通过与行业合作伙伴的合作,打算在竞争前研究中推广此类计算解决方案。近年来,AI和ML方法已经影响了社会的许多方面; DATA将开发和应用新的AI/ML方法,用于药物设计和评估的几个方面,例如筛选/检测药物和靶标之间的相互作用,以及考虑到预先存在的条件的治疗定制。DATA的数学算法和方法可以产生工具和解决方案,以解决整个药物设计和治疗监测生命周期中的主要障碍。该中心还通过整合和管理新的/现有的公共和专有医疗保健/制药数据集来开发大型数据库,为新药的建模,开发和评估提供新的资源。DATA将开发计划,以教育下一代数据科学家,特别是那些来自代表性不足的群体的数据科学家。DATA将在学术界之外形成新的合作,将数据科学应用转化为有影响力的产品/服务,从而降低药物开发成本,同时优化治疗方案并改善患者的整体健康状况。这反过来又减轻了患者和提供者的经济/社会负担。DATA将使学员与制药/医疗保健行业领导者之间能够直接互动,为学员提供对现实世界需求的宝贵见解。DATA将通过直接培训未来的劳动力来满足对更多具有数据科学、药物设计和治疗评估综合专业知识的毕业生的迫切需求。数据集成、策展和存储过程将在第一阶段(和第二阶段,如适用)的范围和持续时间之外继续进行。DATA打算继续收集、整理、整合和预处理DATA当前和未来成员共享的数据库以及公开可用的数据库。这些集成数据库的一个子集,其中一些将通过我们的全同态加密方法进行加密,可以根据中心章程规定的指导方针与外部实体共享。数据库和软件工具将由合作伙伴通过一个专门的和安全的网站与文档共享。这个奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
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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Kayvan Najarian其他文献
Self-Reported Sleep Quality and Same-Day Ratings of Health-Related Quality of Life in Individuals With SCI
- DOI:
10.1016/j.apmr.2019.08.064 - 发表时间:
2019-10-01 - 期刊:
- 影响因子:
- 作者:
Noelle Carlozzi;Nicholas Boileau;Ivan Molton;Dawn Ehde;Kayvan Najarian;Jennifer Miner;Anna Kratz - 通讯作者:
Anna Kratz
Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure
识别数字孪生以指导心力衰竭诊断和预后的可解释人工智能
- DOI:
10.1038/s41746-025-01501-9 - 发表时间:
2025-02-18 - 期刊:
- 影响因子:15.100
- 作者:
Feng Gu;Andrew J. Meyer;Filip Ježek;Shuangdi Zhang;Tonimarie Catalan;Alexandria Miller;Noah A. Schenk;Victoria E. Sturgess;Domingo Uceda;Rui Li;Emily Wittrup;Xinwei Hua;Brian E. Carlson;Yi-Da Tang;Farhan Raza;Kayvan Najarian;Scott L. Hummel;Daniel A. Beard - 通讯作者:
Daniel A. Beard
796: COMPUTER VISION MEASUREMENT OF DISEASE SEVERITY DISTRIBUTION OUTPERFORMS TRADITIONAL ENDOSCOPIC SCORING FOR DETECTING THERAPEUTIC RESPONSE IN A CLINICAL TRIAL OF USTEKINUMAB FOR ULCERATIVE COLITIS
- DOI:
10.1016/s0016-5085(22)60462-1 - 发表时间:
2022-05-01 - 期刊:
- 影响因子:
- 作者:
Ryan Stidham;Heming Yao;Reza Soroushmehr;Jonathan Gryak;Tadd Hiatt;Michael D. Rice;Shrinivas Bishu;Louis R. Ghanem;Aleksandar Stojmirovic;Jan Wehkamp;Xiaoying Wu;Najat Khan;Kayvan Najarian - 通讯作者:
Kayvan Najarian
Mo1736 PREDICTING REMISSION EARLY IN ULCERATIVE COLITIS CLINICAL TRIALS USING COMPUTER VISION ANALYSIS OF ENDOSCOPIC VIDEO
- DOI:
10.1016/s0016-5085(23)03046-9 - 发表时间:
2023-05-01 - 期刊:
- 影响因子:
- 作者:
Ryan Stidham;Cristian Minoccheri;Sophia Tesic;Lingrui Cai;Shuyang Cheng;Flora Rajaei;Tadd Hiatt;Michael D. Rice;Shrinivas Bishu;Jan Wehkamp;Najat Khan;Tommaso Mansi;Xiaoying Wu;Weiwei Schultz;Aleksandar Stojmirovic;Louis R. Ghanem;Kayvan Najarian - 通讯作者:
Kayvan Najarian
353 AUTOMATED DIGITAL ULCER QUANTITATION IN COLONOSCOPY IS BETTER ASSOCIATED WITH CLINICAL REMISSION THAN CONVENTIONAL ENDOSCOPIC SCORING IN CROHN'S DISEASE
- DOI:
10.1016/s0016-5085(23)01106-x - 发表时间:
2023-05-01 - 期刊:
- 影响因子:
- 作者:
Ryan Stidham;Shuyang Cheng;Lingrui Cai;Flora Rajaei;Cristian Minoccheri;Tadd Hiatt;Michael D. Rice;Shrinivas Bishu;Jan Wehkamp;Weiwei Schultz;Xiaoying Wu;Najat Khan;Tommaso Mansi;Aleksandar Stojmirovic;Louis R. Ghanem;Kayvan Najarian - 通讯作者:
Kayvan Najarian
Kayvan Najarian的其他文献
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{{ truncateString('Kayvan Najarian', 18)}}的其他基金
IUCRC Planning Grant University of Michigan – Ann Arbor (UM): Center for Secured Computation for Drug Discovery and Repurposing (SCDDR)
IUCRC 规划拨款密歇根大学 – 安娜堡 (UM):药物发现和再利用安全计算中心 (SCDDR)
- 批准号:
2051997 - 财政年份:2021
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
SCH: INT: Improving Care for Heart Failure Patients Using Tropical Geometry and Soft Computing
SCH:INT:利用热带几何和软计算改善心力衰竭患者的护理
- 批准号:
2014003 - 财政年份:2020
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
BIGDATA: F: Algorithms for Tensor-Based Modeling of Large Scale Structured Data
BIGDATA:F:大规模结构化数据基于张量的建模算法
- 批准号:
1837985 - 财政年份:2018
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
SCH: INT: Data-In-Motion Prediction and Assessment of Acute Respiratory Distress Syndrome
SCH:INT:急性呼吸窘迫综合征的动态数据预测和评估
- 批准号:
1722801 - 财政年份:2017
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
PFI: AIR-TT: Prototype Scale-up for Traumatic Pelvic and Abdominal Injury Decision Support System (DSS)
PFI:AIR-TT:创伤性骨盆和腹部损伤决策支持系统 (DSS) 的原型放大
- 批准号:
1500124 - 财政年份:2015
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
III-CXT: Information Integration and Processing for Computer-Aided Trauma Decision Making
III-CXT:计算机辅助创伤决策的信息集成和处理
- 批准号:
0758410 - 财政年份:2007
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
III-CXT: Information Integration and Processing for Computer-Aided Trauma Decision Making
III-CXT:计算机辅助创伤决策的信息集成和处理
- 批准号:
0713419 - 财政年份:2007
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
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