Indexing, Mining and Modeling Spatio-Temporal Patterns of Gene Expressions

基因表达时空模式的索引、挖掘和建模

基本信息

  • 批准号:
    0640543
  • 负责人:
  • 金额:
    $ 133.2万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2007
  • 资助国家:
    美国
  • 起止时间:
    2007-08-15 至 2011-07-31
  • 项目状态:
    已结题

项目摘要

Carnegie Mellon University is awarded a grant to develop novel machine learning and data mining methods to find spatio-temporal patterns of gene expressions in complex biological contexts of higher eukaryotic organisms. The focus will be on mining in situ hybridization (ISH) images of multi-cell systems and on capturing the cell-level histological context of gene expression in Drosophila embryos. The challenge is to design a good feature extraction function, distance measure, text/image data fusion methods, and spatio-temporal models, which to our knowledge remain very underdeveloped, for embryonic ISH images in Drosophila. The main novelties are: (a) more salient feature extraction based on state-of-the-art image processing techniques such as mathematical morphology, a variety of filters, wavelets, graph-theoretic and probabilistic segmentations, etc.; (b) novel graph-based methods and probabilistic models for image/text fusion and cross-modal querying; (c) novel latent- space models capturing higher-level "semantic similarity" rather than direct feature similarity of functionally or behaviorally similar genes in variable morphology contexts, (d) Kalman filters and non-linear dynamic models, to model and predict spatio-temporal evolutions of gene expression. Moreover a repository of ISH images will be created with the expressions of all known genes in the Drosophila genome from public sources. The structured database can be searched 'by image example' or by keyword (all automatically derived). Systematic profiling of in situ hybridization images of gene expression patterns will attract high interest. Powerful and sophisticated computer algorithms will be needed to analyze these data. The proposed system will meet these needs and will provide a better understanding of embryo development as well as which genes/proteins affect what. This project is a necessary step towards the ultimate, long term goal, of understanding the molecular mechanism of embryo development, and the unraveling of the gene regulation network involved in this process. It also offers a new image-based platform for genetics and developmental biology education.
卡内基梅隆大学获得了一笔赠款,用于开发新的机器学习和数据挖掘方法,以在高等真核生物的复杂生物背景下找到基因表达的时空模式。重点将是挖掘多细胞系统的原位杂交(ISH)图像,并捕获果蝇胚胎中基因表达的细胞水平组织学背景。面临的挑战是设计一个良好的特征提取功能,距离测量,文本/图像数据融合方法,和时空模型,据我们所知,仍然非常不发达,胚胎ISH图像在果蝇。主要的创新点是:(a)根据最新的图像处理技术,例如数学形态学、各种滤波器、小波、图论和概率分割等,提取更显著的特征; (b)用于图像/文本融合和跨模态查询的新颖的基于图的方法和概率模型;(c)新颖的潜在空间模型,其捕获更高级别的“语义相似性”,而不是可变形态学背景中的功能或行为相似基因的直接特征相似性;(d)卡尔曼滤波器和非线性动态模型,以建模和预测基因表达的时空演变。此外,将创建一个ISH图像库,其中包含来自公共来源的果蝇基因组中所有已知基因的表达。 结构化的数据库可以搜索“通过图像的例子”或通过关键字(所有自动派生)。基因表达模式的原位杂交图像的系统分析将吸引高度的兴趣。分析这些数据将需要强大而复杂的计算机算法。拟议的系统将满足这些需求,并将提供更好的了解胚胎发育以及哪些基因/蛋白质影响什么。该项目是实现最终长期目标的必要一步,即了解胚胎发育的分子机制,并解开参与这一过程的基因调控网络。它还为遗传学和发育生物学教育提供了一个新的基于图像的平台。

项目成果

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Eric Xing其他文献

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
您的数据对 GPT 有何价值?
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sang Keun Choe;Hwijeen Ahn;Juhan Bae;Kewen Zhao;Minsoo Kang;Youngseog Chung;Adithya Pratapa;W. Neiswanger;Emma Strubell;Teruko Mitamura;Jeff Schneider;Eduard Hovy;Roger Grosse;Eric Xing
  • 通讯作者:
    Eric Xing
Applications of artificial intelligence in public health: analyzing the built environment and addressing spatial inequities
  • DOI:
    10.1007/s10389-025-02444-x
  • 发表时间:
    2025-03-19
  • 期刊:
  • 影响因子:
    1.600
  • 作者:
    Ana Luiza Favarão Leão;Bernard Banda;Eric Xing;Sanketh Gudapati;Adeel Ahmad;Jonathan Lin;Srikumar Sastry;Nathan Jacobs;Rodrigo Siqueira Reis
  • 通讯作者:
    Rodrigo Siqueira Reis
An exploratory study of self-supervised pre-training on partially supervised multi-label classification on chest X-ray images
胸部X射线图像部分监督多标签分类自监督预训练的探索性研究
  • DOI:
    10.1016/j.asoc.2024.111855
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    8.7
  • 作者:
    Nanqing Dong;Michael Kampffmeyer;Haoyang Su;Eric Xing
  • 通讯作者:
    Eric Xing

Eric Xing的其他文献

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{{ truncateString('Eric Xing', 18)}}的其他基金

III: Small: Multiple Device Collaborative Learning in Real Heterogeneous and Dynamic Environments
III:小:真实异构动态环境中的多设备协作学习
  • 批准号:
    2311990
  • 财政年份:
    2023
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant
ML Basis for Intelligence Augmentation:Toward Personalized Modeling, Reasoning under Data-Knowledge Symbiosis, and Interpretable Interaction for AI-assisted Human Decision-making
智能增强的机器学习基础:面向人工智能辅助人类决策的个性化建模、数据知识共生下的推理和可解释的交互
  • 批准号:
    2040381
  • 财政年份:
    2021
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Continuing Grant
Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases
合作研究:SCH:值得信赖且可解释的人工智能治疗神经退行性疾病
  • 批准号:
    2123952
  • 财政年份:
    2021
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant
CNS Core: Small: Toward Globally-Optimal Resource Distribution and Computation Acceleration in Multi-Tenant and Heterogeneous Machine Learning Systems
CNS 核心:小型:在多租户和异构机器学习系统中实现全局最优资源分配和计算加速
  • 批准号:
    2008248
  • 财政年份:
    2020
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant
III: Small: A New Approach to Latent Space Learning with Diversity-Inducing Regularization and Applications to Healthcare Data Analytics
III:小型:具有多样性诱导正则化的潜在空间学习新方法及其在医疗保健数据分析中的应用
  • 批准号:
    1617583
  • 财政年份:
    2016
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant
XPS: FULL: Broad-Purpose, Aggressively Asynchronous and Theoretically Sound Parallel Large-scale Machine Learning
XPS:FULL:用途广泛、积极异步且理论上合理的并行大规模机器学习
  • 批准号:
    1629559
  • 财政年份:
    2016
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant
BIGDATA: F: DKA: Collaborative Research: Theory and Algorithms for Parallel Probabilistic Inference with Big Data, via Big Model, in Realistic Distributed Computing Environments
BIGDATA:F:DKA:协作研究:在现实分布式计算环境中通过大模型进行大数据并行概率推理的理论和算法
  • 批准号:
    1447676
  • 财政年份:
    2014
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant
III: Small: Collaborative Research: Efficient, Nonparametric and Local-Minimum-Free Latent Variable Models: With Application to Large-Scale Computer Vision and Genomics
III:小型:协作研究:高效、非参数和局部最小自由潜变量模型:应用于大规模计算机视觉和基因组学
  • 批准号:
    1218282
  • 财政年份:
    2012
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Continuing Grant
III: Small: Collaborative Research: Using Large-Scale Image Data for Online Social Media Analysis
III:小:协作研究:使用大规模图像数据进行在线社交媒体分析
  • 批准号:
    1115313
  • 财政年份:
    2011
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant
Collaborative Research: Discovering and Exploiting Latent Communities in Social Media
协作研究:发现和利用社交媒体中的潜在社区
  • 批准号:
    1111142
  • 财政年份:
    2011
  • 资助金额:
    $ 133.2万
  • 项目类别:
    Standard Grant

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