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

• 批准号: 0640543
• 负责人: Eric Xing
• 金额: $ 133.2万
• 依托单位: Carnegie-Mellon University
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0640543&HistoricalAwards=false
• 关键词: Indexing; Mining; Modeling; Spatio; Temporal

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。