Computational Methods for Expression Image Analysis

表达图像分析的计算方法

• 批准号: 8051993
• 负责人: Sudhir Kumar
• 金额: $ 32.47万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8051993
• 关键词: Animal Model; Animals; Biochemical; Biological; Biomedical Research; Cells; Color; Communities; Complex; Computational Biology; Computing Methodologies; Controlled Vocabulary; Data; Development; Developmental Biology; Drosophila genus; Drosophila melanogaster; Effectiveness; Embryo; Fishes; Future; Gene Proteins; Genome; Genomic Segment; Genomics; Human; Human Development; Image; Image Analysis; Imaging Techniques; Imaging technology; Individual; Informatics; Investigation; Joints; Knowledge; Lead; Learning; Location; Machine Learning; Maps; Methods; Mining; Modeling; Ontology; Organ; Organism; Pattern; Proteins; Psychological Transfer; Research; Resolution; Scientist; Sequence Analysis; Staging; Statistical Methods; System; Techniques; Testing; Text; Time; Training; Translating; Vocabulary; comparative; functional genomics; gene interaction; genome sequencing; genome-wide; human disease; imprint; insight; next generation; novel; spatiotemporal; text searching; tool; vision development

DESCRIPTION (provided by applicant): Recent advances in high-throughput bio-imaging technologies are enabling scientists to capture the spatio- temporal patterns of gene expression in cells, organs and individuals in efforts to generate a more comprehensive picture of genome function. Today, images capturing gene expression and protein localization patterns have unprecedented spatial resolution, resulting in high-quality maps (expression images) in model organisms. However, computational biology of gene expression patterns lags far behind genome informatics. Automated and efficient tools for analyzing expression images are a prerequisite for generating biological insights into gene functions, interactions and networks for the next generation of scientists. This project focuses on the development of novel tools and techniques for large-scale biological annotation and comparative analysis of gene expression patterns in the early development of a model organism (Drosophila melanogaster; the fruit fly). This choice is important because the fruit fly is a canonical model organism for understanding the development of humans and other animals. The availability of >100,000 images that capture gene expression patterns, provides an opportunity to examine the similarities and differences in the expression of the developing embryos of fruit fly genes, many of which show a very high sequence and biochemical function similarity to humans proteins. Three primary aims of the proposed research are as follows: (a) Develop machine learning methods that will use the existing, but coarse, knowledge in training, and will produce refined and new stage information for existing and future images. The knowledge of the precise developmental stage is important because it enables the biologically-meaningful mining of genes with similar spatial patterns, calculating the developmental trajectories of gene expressions, facilitating stage-sensitive textual annotation of expressions captured in images, and building genome-wide expression pattern maps at critical junctures in development. (b) Develop machine learning methods to describe expression patterns in words by using the existing controlled vocabulary. These descriptions will enable the use of efficient text-mining tools to identify genes expressed in similar organs and their precursors. These descriptions will also provide a better comparison of expression patterns across species, because many efforts in the scientific community relate organism specific controlled vocabularies with each other. (c) Develop transfer learning techniques for stage and text annotation that can be used for images generated from future techniques. This is important because traditionally, machine learning approaches assume that the training data and test data are drawn from the same distribution. However, new bio-imaging techniques are producing higher resolution data with substantially different color and intensity distributions, and robust methods that apply across techniques are desired.

描述（由申请人提供）：高通量生物成像技术的最新进展使科学家能够捕获细胞、器官和个体中基因表达的时空模式，以产生更全面的基因组功能图。如今，捕捉基因表达和蛋白质定位模式的图像具有前所未有的空间分辨率，从而在模式生物中产生高质量的图谱（表达图像）。然而，基因表达模式的计算生物学远远落后于基因组信息学。用于分析表达图像的自动化和高效的工具是为下一代科学家产生对基因功能，相互作用和网络的生物学见解的先决条件。该项目的重点是开发新的工具和技术，用于大规模生物注释和比较分析模式生物体（果蝇;果蝇）早期发育中的基因表达模式。这个选择很重要，因为果蝇是了解人类和其他动物发展的典型模式生物。捕获基因表达模式的> 100，000个图像的可用性提供了检查果蝇基因发育胚胎表达的相似性和差异的机会，其中许多显示出与人类蛋白质非常高的序列和生化功能相似性。拟议研究的三个主要目标如下：（a）开发机器学习方法，在训练中使用现有但粗糙的知识，并为现有和未来的图像生成精细和新的阶段信息。精确的发育阶段的知识是重要的，因为它使具有相似空间模式的基因的生物学上有意义的挖掘，计算基因表达的发育轨迹，促进图像中捕获的表达的阶段敏感的文本注释，并在发育的关键节点构建全基因组表达模式图。(b)开发机器学习方法，通过使用现有的受控词汇表来描述单词中的表达模式。这些描述将使有效的文本挖掘工具的使用，以确定在类似的器官及其前体表达的基因。这些描述还将提供跨物种表达模式的更好比较，因为科学界的许多努力将生物体特异性控制词汇彼此关联。(c)开发用于阶段和文本注释的迁移学习技术，可用于从未来技术生成的图像。这很重要，因为传统上，机器学习方法假设训练数据和测试数据来自相同的分布。然而，新的生物成像技术正在产生具有显著不同的颜色和强度分布的更高分辨率的数据，并且期望跨技术应用的鲁棒方法。