Computational Methods for Expression Image Analysis

表达图像分析的计算方法

基本信息

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
项目类别：
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

项目摘要

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，以努力生成更全面的基因组功能图景。如今，捕获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、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。该项目重点开发新工具和技术，用于模式生物（黑腹果蝇；果蝇）早期发育过程中基因表达模式的大规模生物注释和比较分析。这一选择很重要，因为果蝇是了解人类和其他动物发育的典型模式生物。捕获基因表达模式的超过 100,000 张图像为检查果蝇基因发育胚胎表达的相似性和差异提供了机会，其中许多基因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，该方法将在训练中使用现有的但粗略的知识，并为现有和未来的图像生成精炼的新阶段信息。了解精确的发育阶段非常重要，因为它能够对具有相似空间模式的基因进行具有生物学意义的挖掘，计算基因表达的发育轨迹，促进对图像中捕获的表达进行阶段敏感的文本注释，并构建全基因组表达发展关键时刻的模式图。 (b)利用现有的受控词汇开发机器学习方法来描述单词的表达模式。这些描述将使得能够使用有效的文本挖掘工具来识别在相似器官及其前体中表达的基因。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定的受控词汇相互联系起来。 (c) 开发用于舞台和文本注释的迁移学习技术，可用于通过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着不同的颜色和强度分布的更高分辨率的数据，并且需要适用于各种技术的稳健方法。