课题基金 / 基金详情

NRT-DESE: Network Biology: From Data to Information to Insights

NRT-DESE: Network Biology: From Data to Information to Insights
NRT-DESE:网络生物学:从数据到信息到见解
批准号:
1632976
负责人:
Michelle Girvan
金额:
$295.99万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-15 至 2022-08-31

项目摘要

项目成果

Michelle Girvan的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
An urgent issue facing today's researchers in the life sciences is coping with the data explosion resulting from the advent of powerful new technologies. More data does not yield better information without the interdisciplinary tools required for such a transformation. This National Science Foundation Research Traineeship (NRT) award to the University of Maryland, College Park will build an innovative, cross-disciplinary model for graduate education that addresses this challenge by preparing students to pursue a range of STEM careers at the nexus of the computer, physical, and life sciences. Trainees will learn to combine physics-style quantitative modeling with data processing, analysis, and visualization methods from computer science to gain deeper insights into the principles governing living systems. The project anticipates training approximately sixty (60) PhD students, including thirty-five (35) funded trainees, from the physical, computer, and life sciences.Understanding how data-derived interaction patterns can give insights into complex biological phenomena is the research focus of this program. Through an innovative combination of cross-disciplinary training, collaborative research, and outreach activities, NRT trainees will become experts in the process of transforming raw biological data into useful information from which new biological insights can be inferred. Participants will receive training in four different areas of network analysis: quantitative metrics for biological networks; mechanistic models of biological networks; network statistics and machine learning for biological applications; and visualization techniques for large, complex, biological datasets. This training will provide the foundation for research in one or more of three application areas, covering a wide range of biological scales: biomolecular networks; neuronal networks; and ecological/behavioral networks. Research experiences, interdisciplinary coursework, peer-to-peer tutorials, and internships with partners will provide graduate students with the skills needed to communicate complex scientific ideas to diverse audiences in order to maximize impact. Outreach activities will extend the benefits of the program to undergraduates, middle/high school students, and to the public at large.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.
期刊论文(65)
专著(0)
科研奖励(0)
会议论文
A Neurocomputational Model of Posttraumatic Stress Disorder
创伤后应激障碍的神经计算模型
DOI: 10.1109/ner49283.2021.9441345
发表时间: 2021
期刊: International IEEE/EMBS Conference on Neural Engineering 2021
影响因子: --
作者: [Davis, Gregory P., Katz, Garrett E., Soranzo, Daniel, Allen, Nathaniel, Reinhard, Matthew J., Gentili, Rodolphe J., Costanzo, Michelle E., Reggia, James A.]
通讯作者: Reggia, James A.
Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics
通过人工智能辅助分子动力学发现蛋白质构象灵活性
DOI: 10.1021/acs.jpcb.0c03985
发表时间: 2020
期刊: The Journal of Physical Chemistry B
影响因子: --
作者: [Smith, Zachary, Ravindra, Pavan, Wang, Yihang, Cooley, Rory, Tiwary, Pratyush]
通讯作者: Tiwary, Pratyush
Determination of disease phenotypes and pathogenic variants from exome sequence data in the CAGI 4 gene panel challenge.
在CAGI 4基因面板挑战中,从外显子组序列数据中确定疾病表型和致病变异。
DOI: 10.1002/humu.23249
发表时间: 2017-09
期刊: Human mutation
影响因子: 3.9
作者: [Kundu K, Pal LR, Yin Y, Moult J]
通讯作者: Moult J
Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes
不同选择过程下进化的基因调控网络模型中的相变和相配性
DOI: 10.1098/rsif.2020.0790
发表时间: 2021
期刊: Journal of The Royal Society Interface
影响因子: 3.9
作者: [Alexander, Brandon, Pushkar, Alexandra, Girvan, Michelle]
通讯作者: Girvan, Michelle
39
    REU Site: Training and Research Experiences in Nonlinear Dynamics (TREND)
    • 批准号:
      1461089
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $35.29万
    • 财政年份:
      2015
    • 负责人:
      Michelle Girvan
    • 依托单位:
    Research Experiences for Undergraduates (REU) Site: Training and Research Experiences in Nonlinear Dynamics (TREND)
    • 批准号:
      1156454
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $35.4万
    • 财政年份:
      2012
    • 负责人:
      Michelle Girvan
    • 依托单位:
    Conference: Dynamics Days 2012
    • 批准号:
      1159421
    • 项目类别:
      Standard Grant
    • 资助金额:
      $1.5万
    • 财政年份:
      2011
    • 负责人:
      Michelle Girvan
    • 依托单位:
    海外基金