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Pre-Doctoral Training Program in Computational Medicine

计算医学博士前培训项目

基本信息

批准号：
10190960
负责人：
Feilim C Mac Gabhann
金额：
$ 43.87万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10190960
关键词：
Medicine Training Programs pre-doctoral

项目摘要

Project Summary We will create the Johns Hopkins University “Pre-Doctoral Training Program in Computational Medicine”, in the research area of Bioinformatics and Computational Biology. The participating departments are Biomedical Engineering and Applied Mathematics & Statistics. We are requesting 6 pre-doctoral training slots per year. Our trainees will learn how to: develop models of biological systems in health and disease; constrain these models using data collected from patients; apply models to deliver improved diagnoses and therapies. They will learn this through a combination of focused course work, and dissertation research in the laboratories of program faculty. They will gain experience with oral presentation, grant writing and teaching. They will receive responsible conduct of research training that goes beyond current offerings to meet the needs of computational medicine researchers. They will be part of a community of students and faculty. We will put in place novel programs that increase diversity in training by creating an applicant pool of undergraduates who have done substantial research rotations with our program faculty. Graduates will be well prepared and sought after to fill the growing need for researchers trained in computational medicine in both industry and academia. Support of our program will contribute to achieving the goal of precision medicine.

项目摘要我们将创建约翰霍普金斯大学“计算医学博士前培训计划”，生物信息学和计算生物学的研究领域。参与部门为生物医学工程和应用数学与统计学。我们每年申请6个博士前培训名额。我们学员将学习如何：开发健康和疾病的生物系统模型;约束这些模型使用从患者收集的数据;应用模型来提供改进的诊断和治疗。他们将学习这是通过将重点课程作业和项目实验室的论文研究相结合来实现的教师。他们将获得口头报告，赠款写作和教学的经验。他们将负责开展超越当前产品的研究培训，以满足计算医学的需求研究人员他们将成为学生和教师社区的一部分。我们将实施新颖的方案，通过建立一个由做过大量研究的本科生组成的申请人库来增加培训的多样性与我们的项目教师轮换。毕业生将做好充分的准备，并寻求填补日益增长的需求，在工业界和学术界接受计算医学培训的研究人员。对我们项目的支持将有助于实现精准医疗的目标。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Spectral clustering of risk score trajectories stratifies sepsis patients by clinical outcome and interventions received.

DOI：
10.7554/elife.58142
发表时间：
2020-09-22
期刊：
eLife
影响因子：
7.7
作者：
Liu R;Greenstein JL;Fackler JC;Bembea MM;Winslow RL
通讯作者：
Winslow RL

Data-driven Discovery of Emergent Behaviors in Collective Dynamics

DOI：
10.1016/j.physd.2020.132542
发表时间：
2019-12
期刊：
Physica D. Nonlinear phenomena
影响因子：
0
作者：
M. Maggioni;Jason D Miller;Ming Zhong
通讯作者：
M. Maggioni;Jason D Miller;Ming Zhong

Investigation of Architectures for Models of Neural Responses to Electrical Brain Stimulation.

脑电刺激神经反应模型架构的研究。

DOI：
10.1109/embc.2019.8857455
发表时间：
2019
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
0
作者：
Steinhardt,Cynthia;Sacre,Pierre;Inati,SaraK;Sarma,SrideviV;Zaghloul,KareemA
通讯作者：
Zaghloul,KareemA

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.

通过结合心房计算流体动力学模拟和功能性患者数据进行全面的中风风险评估。

DOI：
10.1101/2024.01.11.575156
发表时间：
2024
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Zingaro,Alberto;Ahmad,Zan;Kholmovski,Eugene;Sakata,Kensuke;Dede',Luca;Morris,AlanK;Quarteroni,Alfio;Trayanova,NataliaA
通讯作者：
Trayanova,NataliaA

Characterizing and predicting cortical evoked responses to direct electrical stimulation of the human brain.

表征和预测皮质对人脑直接电刺激的诱发反应。

DOI：
10.1016/j.brs.2020.05.001
发表时间：
2020
期刊：
Brain stimulation
影响因子：
7.7
作者：
Steinhardt,CynthiaR;Sacré,Pierre;Sheehan,TimothyC;Wittig,JohnH;Inati,SaraK;Sarma,Sridevi;Zaghloul,KareemA
通讯作者：
Zaghloul,KareemA