CAREER: High-dimensional inference and applications to modern biology
职业:高维推理及其在现代生物学中的应用
基本信息
- 批准号:2142476
- 负责人:
- 金额:$ 40万
- 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:2022-07-01 至 2027-06-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
In recent years, a burgeoning field of high-dimensional statistical inference has witnessed astounding advances, providing new theoretical tools to characterize exact distributional behavior for an increasingly large class of statistical and machine-learning methods. These advances hold the promise of improved statistical procedures with more precise quantifications of uncertainty across many fields of modern biology. This research will extend the scope of these high-dimensional inferential methods, which currently remain restricted to more stylized statistical models, to address a broader range of scientific problems having complex latent structure. The research will also enable the PI to continue his educational outreach activities in the K-12 levels in Connecticut public schools, as well as his experimentation in the teaching of introductory courses at Yale University by focusing the discussion of statistical concepts and ideas on motivating real-life examples.On the theoretical front, this research will improve our understanding of mean-field phenomena in non-i.i.d. contexts, including disordered systems and spin glass models with statistically dependent couplings, as well as variational Bayesian approximations to regression models with correlated designs. This research will also further our understanding of asymptotic freeness phenomena for random matrix models arising in statistical settings. On the applications front, this research will improve our understanding of likelihood-based inference for molecular structure determination in cryo-electron microscropy, and investigate possibilities for more robust and efficient reconstruction algorithms. This research will also develop new Bayes and empirical Bayes procedures for fine-mapping of genetic causal variants and for dimensionality reduction of genetic sequence data.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.
近年来,高维统计推断的一个新兴领域取得了惊人的进步,为越来越多的统计和机器学习方法提供了新的理论工具来表征准确的分布行为。这些进展有望改进统计程序,对现代生物学许多领域的不确定性进行更精确的量化。这项研究将扩大这些高维推理方法的范围,目前这些方法仍然局限于更程式化的统计模型,以解决具有复杂潜在结构的更广泛的科学问题。这项研究还将使PI能够继续他在康涅狄格州公立学校的K-12级别的教育外展活动,以及他在耶鲁大学的入门课程教学中的实验,通过集中讨论统计概念和想法来激发现实生活中的例子。在理论方面,本研究将提高我们对非I.I.D.平均场现象的理解。背景,包括具有统计相关耦合的无序系统和自旋玻璃模型,以及具有相关设计的回归模型的变分贝叶斯近似。这一研究也将进一步加深我们对统计背景下随机矩阵模型的渐近自由性现象的理解。在应用方面,这项研究将提高我们对基于似然推理的低温电子微结构确定的理解,并探索更健壮和高效的重建算法的可能性。这项研究还将开发新的贝叶斯和经验贝叶斯程序,用于精细绘制遗传因果变量图和基因序列数据降维。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
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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Zhou Fan其他文献
Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network
通过深度循环神经网络识别多个时间尺度的大脑网络
- DOI:
10.1109/jbhi.2018.2882885 - 发表时间:
2019-11 - 期刊:
- 影响因子:7.7
- 作者:
Cui Yan;Zhao Shijie;Wang Han;Xie Li;Chen Yaowu;Han Junwei;Guo Lei;Zhou Fan;Liu Tianming - 通讯作者:
Liu Tianming
Highly sensitive, label-free and real-time detection of alpha-fetoprotein using a silicon nanowire biosensor
使用硅纳米线生物传感器高灵敏度、无标记、实时检测甲胎蛋白
- DOI:
10.3109/00365513.2015.1060519 - 发表时间:
2015-08 - 期刊:
- 影响因子:2.1
- 作者:
Zhou Fan;Li Zengyao;Bao Zengtao;Feng Kang;Zhang Ye;Wang Tong - 通讯作者:
Wang Tong
Observation of filament-like structures in ELMy H-mode plasma with a VUV imaging system developed on the EAST tokamak
使用 EAST 托卡马克开发的 VUV 成像系统观察 ELMy H 模式等离子体中的丝状结构
- DOI:
10.1088/2058-6272/ab1b1b - 发表时间:
2019-07 - 期刊:
- 影响因子:1.7
- 作者:
Zhou Fan;Ming Tingfeng;Wang Yumin;Long Feifei;Zhuang Qing;Liu Shaocheng;Li Guoqiang;Gao Xiang - 通讯作者:
Gao Xiang
Real-time monitoring of D-Ala-D-Ala dipeptidase activity of VanX in living bacteria by isothermal titration calorimetry
等温滴定量热法实时监测活菌中VanX的D-Ala-D-Ala二肽酶活性
- DOI:
10.1016/j.ab.2019.05.002 - 发表时间:
2019 - 期刊:
- 影响因子:2.9
- 作者:
Lv Miao;Zhang Yue Juan;Zhou Fan;Ge Ying;Zhao Mu Han;Liu Ya;Yang Ke Wu - 通讯作者:
Yang Ke Wu
Genome-wide screening of budding yeast with honokiol to associate mitochondrial function with lipid metabolism
使用和厚朴酚对芽殖酵母进行全基因组筛选,将线粒体功能与脂质代谢联系起来
- DOI:
10.1111/tra.12611 - 发表时间:
2018 - 期刊:
- 影响因子:4.5
- 作者:
Zhu Xiaolong;Cai Juan;Zhou Fan;Wu Zulin;Li Dan;Li Youbin;Xie Zhiping;Zhou Yiting;Liang Yongheng - 通讯作者:
Liang Yongheng
Zhou Fan的其他文献
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{{ truncateString('Zhou Fan', 18)}}的其他基金
Non-Convex Landscapes and High-Dimensional Latent Variable Models
非凸景观和高维潜变量模型
- 批准号:
1916198 - 财政年份:2019
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
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