Matheamatical Sciences: University-Industry Postdoctoral Fellow Support for Research in Optimal Design and Control ofFluids
数学科学:大学-工业界博士后研究员支持流体优化设计和控制研究
基本信息
- 批准号:9508773
- 负责人:
- 金额:$ 7.1万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:1995
- 资助国家:美国
- 起止时间:1995-07-01 至 1999-03-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
9501570 Liu Abstract (technical) Multiple sequence alignment is crucial in research on the structure and function of gene products, through promoting the detection and description of sequence motifs; aiding efforts at protein modeling, structure prediction and engineering; and shedding light on molecular evolution and phylogeny construction in molecular systematics. We recently presented new local alignment algorithms (Lawrence et al. 1993, 1994) based on Bayesian modeling and Gibbs sampling. In this project, we propose to develop several extensions of our original Bayesian model to accommodate various practical situations, and to investigate several statistical problems arisen from our study of the alignment problem. On the applied side, we wish to accomplish the following: (1) relax the existence assumption and develop Bayesian models to simultaneously detect and align the common multiple motifs in the sequences; (2) combine the idea of Markovian structure modeling of sequence data (e.g., hidden Markov Model) with the block-based method of Lawrence et. al. (1993) to enhance the alignment of multiple motifs; (3) modify and apply the methodology developed previously and in this project to several other important biological problems: the detection of subtle sequence signals in noncoding DNA region; the prediction of protein secondary structures; and the combination of threading and alignment. On the theoretical side, we attempt to (4) understand the nature and potential of predictive updating in connection with the bootstrap, the Bayesian bootstrap, and general nonparametric problems; (5) develop model selection criteria that are useful for determining pattern width in alignment problems; (6) study the influence of prior specifications on the results and frequency properties of these results in Bayesian classification problems; and finally, (7) study convergence properties of several sampling algorithms. During the award period, I will develop my own styles on teaching introductory probability and statistics courses with an emphasis on statistical thinking, and get involved in a program designed to attract talented undergraduates into our field. I will also make an effort to develop new graduate level courses on current research areas such as image processing, missing data, Markov Chain Monte Carlo, genetics and biology. My guideline for directing graduate students is: independence, connections, and creativity. 9501570 Liu Abstract (nontechnical) A wealth of data concerning life's basic molecules, proteins and nucleic acids, has emerged from the biotechnology revolution. The human genome project has accelerated the growth of these data. Multiple observations of homologous protein or nucleic acid sequences from different organisms are often available. However, since mutations and sequence errors misalign these data, multiple sequence alignment has become an essential and valuable tool for understanding the structures and functions of these molecule, aiding efforts at protein modeling, structure prediction and engineering; and shedding light on molecular evolution and phylogeny construction in molecular systematics. Traditional algorithms for finding such alignment are either too computationally expensive so as to limit their applications or too heuristic so that the sensitivity to subtle patterns is lost. We recently presented new local alignment algorithms (Lawrence et al. 1993, 1994) based on a probabilistic model of the sequences and a stochastic simulation technique called the Gibbs sampler. In this project, we propose to develop several extensions of our original probabilistic model to accommodate various practical situations, and to investigate several statistical problems arisen from our study of the alignment problem. On the applied side, we wish to accomplish the following: (1) relax the existence assum ption and develop more general mixture models to simultaneously detect and align common patterns in multiple homologous biological sequences; (2) investigate the connection between our method and other successful approaches such as the hidden Markov modeling; (3) modify and apply the methodology developed previously and in this project to several other important biological problems: the detection of subtle sequence signals in noncoding DNA region; the prediction of protein secondary structures; and the combination of threading and alignment. On the theoretical side, we attempt to (4) understand the nature and potential of the predictive updating, a new iterative simulation method, in connection with the bootstrap, the Bayesian bootstrap, and general nonparametric problems; (5) develop model selection criteria that are useful for determining pattern width in alignment problems; (6) study the influence of prior specifications on the final alignment results; and finally, (7) study convergence properties of several stochastic simulation algorithms. During the award period, I will develop my own styles on teaching introductory probability and statistics courses with an emphasis on statistical thinking, and get involved in a program designed to attract talented undergraduates into our field. I will also make an effort to develop new graduate level courses on current research areas such as image processing, missing data, Markov Chain Monte Carlo, genetics and biology. My guideline for directing graduate students is: independence, connections, and creativity.
摘要(技术)多序列比对通过促进序列基序的检测和描述,在基因产物结构和功能研究中起着至关重要的作用;协助蛋白质建模、结构预测和工程;并对分子系统学中的分子进化和系统发育构建有所启发。我们最近提出了基于贝叶斯建模和吉布斯抽样的新的局部对齐算法(Lawrence et al. 1993,1994)。在这个项目中,我们建议对我们原来的贝叶斯模型进行一些扩展,以适应各种实际情况,并研究我们在研究对齐问题时产生的几个统计问题。在应用方面,我们希望完成以下工作:(1)放宽存在假设,建立贝叶斯模型,同时检测和对齐序列中的共同多重基序;(2)将序列数据的马尔可夫结构建模思想(如隐马尔可夫模型)与Lawrence et. al.(1993)基于块的方法相结合,增强多基元的对齐;(3)修改和应用之前和本项目中开发的方法来解决其他几个重要的生物学问题:检测非编码DNA区域的细微序列信号;蛋白质二级结构的预测;以及穿线和对齐的结合。在理论方面,我们试图(4)理解与自举、贝叶斯自举和一般非参数问题相关的预测更新的本质和潜力;(5)制定模型选择标准,用于确定对齐问题中的图案宽度;(6)研究贝叶斯分类问题中先验规范对结果的影响以及这些结果的频率特性;最后,(7)研究了几种采样算法的收敛性。在获奖期间,我将在以统计思维为重点的概率论和统计入门课程的教学上形成自己的风格,并参与一个旨在吸引有才华的本科生进入我们领域的项目。我还将努力在当前的研究领域开发新的研究生课程,如图像处理,缺失数据,马尔可夫链蒙特卡罗,遗传学和生物学。我指导研究生的准则是:独立、联系和创造性。摘要(非技术)生物技术革命产生了大量关于生命基本分子、蛋白质和核酸的数据。人类基因组计划加速了这些数据的增长。对来自不同生物体的同源蛋白或核酸序列的多次观察通常是可用的。然而,由于突变和序列错误使这些数据不一致,多序列比对已成为了解这些分子结构和功能的重要和有价值的工具,有助于蛋白质建模,结构预测和工程;并对分子系统学中的分子进化和系统发育构建有所启发。寻找这种对齐的传统算法要么计算成本太高,从而限制了它们的应用,要么过于启发式,从而失去了对微妙模式的敏感性。我们最近提出了新的局部比对算法(Lawrence et al. 1993,1994),该算法基于序列的概率模型和称为Gibbs采样器的随机模拟技术。在这个项目中,我们建议对我们的原始概率模型进行一些扩展,以适应各种实际情况,并研究我们在研究对齐问题时产生的几个统计问题。在应用方面,我们希望实现以下目标:(1)放宽存在假设,开发更通用的混合模型,以同时检测和比对多个同源生物序列中的共同模式;(2)研究我们的方法与其他成功方法(如隐马尔可夫建模)之间的联系;(3)修改和应用之前和本项目中开发的方法来解决其他几个重要的生物学问题:检测非编码DNA区域的细微序列信号;蛋白质二级结构的预测;以及穿线和对齐的结合。在理论方面,我们试图(4)了解预测更新的本质和潜力,预测更新是一种新的迭代模拟方法,与自举法、贝叶斯自举法和一般非参数问题有关;(5)制定模型选择标准,用于确定对齐问题中的图案宽度;(6)研究先前规格对最终校直结果的影响;最后,(7)研究了几种随机仿真算法的收敛性。在获奖期间,我将在以统计思维为重点的概率论和统计入门课程的教学上形成自己的风格,并参与一个旨在吸引有才华的本科生进入我们领域的项目。我还将努力在当前的研究领域开发新的研究生课程,如图像处理,缺失数据,马尔可夫链蒙特卡罗,遗传学和生物学。我指导研究生的准则是:独立、联系和创造性。
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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John Burns其他文献
The Brief Evaluation of Adolescents and Children Online (BEACON): Psychometric development of a mental health screening measure for school students.
青少年和儿童在线简要评估(BEACON):针对中学生心理健康筛查措施的心理测量发展。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:3
- 作者:
Ronald M Rapee;Rebecca Kuhnert;Susan H. Spence;Ian Bowsher;John Burns;Jennifer Coen;Julie Dixon;Pauline Kotselas;Catherine Lourey;Lauren F. McLellan;Cathrine Mihalopoulos;Lorna Peters;Traci Prendergast;Tiffany Roos;Danielle Thomas;Viviana M. Wuthrich - 通讯作者:
Viviana M. Wuthrich
The image of accountants: From bean counters to extreme accountants
会计师形象:从精算师到极端会计师
- DOI:
10.1108/09513570910980445 - 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
G. Baldvinsdottir;John Burns;Hanne Nørreklit;R. Scapens - 通讯作者:
R. Scapens
Surgical and nonsurgical treatment of total carotid artery occlusion
- DOI:
10.1016/s0002-9610(85)80108-2 - 发表时间:
1985-03-01 - 期刊:
- 影响因子:
- 作者:
Bhagwan Satiani;John Burns;John S. Vasko - 通讯作者:
John S. Vasko
MP43-20 RATES OF NON-DEFINITIVE MANAGEMENT FOR LOW AND INTERMEDIATE RISK PROSTATE CANCER ARE SIMILAR BETWEEN AFRICAN AMERICANS AND CAUCASIANS
- DOI:
10.1016/j.juro.2017.02.1330 - 发表时间:
2017-04-01 - 期刊:
- 影响因子:
- 作者:
John Burns;John P. Flores;Mazen Alsinnawi;Sydney Akapame;John Massman III;Christopher Porter - 通讯作者:
Christopher Porter
The use of accounting in managing the institutional complexities of a festival organisation pursuing financial and social objectives
使用会计来管理追求财务和社会目标的节日组织的机构复杂性
- DOI:
10.1108/jaoc-09-2020-0126 - 发表时间:
2020 - 期刊:
- 影响因子:1.9
- 作者:
Per Ståle Knardal;John Burns - 通讯作者:
John Burns
John Burns的其他文献
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{{ truncateString('John Burns', 18)}}的其他基金
CREST-PRF: Utilizing innovative 3D reconstruction techniques to enhance our understanding of the biology and ecology of Hawaiian ecosystems
CREST-PRF:利用创新的 3D 重建技术来增强我们对夏威夷生态系统的生物学和生态学的理解
- 批准号:
1720706 - 财政年份:2017
- 资助金额:
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Workforce Preparedness: Moving Into Computer-Integrated Manufacturing
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- 批准号:
9152361 - 财政年份:1991
- 资助金额:
$ 7.1万 - 项目类别:
Standard Grant
US-Austria Cooperative Research On Stabilization and Controlof Distributed Parameter Systems
美奥分布式参数系统稳定与控制合作研究
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8922490 - 财政年份:1990
- 资助金额:
$ 7.1万 - 项目类别:
Standard Grant
US-Argentina Cooperative Research: Identification and Control of Systems with Memory
美阿根廷合作研究:带记忆系统的识别与控制
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8815136 - 财政年份:1989
- 资助金额:
$ 7.1万 - 项目类别:
Standard Grant
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8850176 - 财政年份:1988
- 资助金额:
$ 7.1万 - 项目类别:
Continuing Grant
Computational Methods For Identification and Optimal Control of Hereditary Systems
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8109245 - 财政年份:1981
- 资助金额:
$ 7.1万 - 项目类别:
Continuing Grant
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7506993 - 财政年份:1975
- 资助金额:
$ 7.1万 - 项目类别:
Standard Grant
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