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

卡内基梅隆大学获得一笔赠款，用于开发新的机器学习和数据挖掘方法，以发现高等真核生物复杂生物学背景下基因表达的时空模式。重点将放在挖掘多细胞系统的原位杂交(ISH)图像和捕捉果蝇胚胎基因表达的细胞水平组织学背景上。我们面临的挑战是为果蝇的胚胎ISH图像设计一个好的特征提取函数、距离度量、文本/图像数据融合方法和时空模型，这些都是我们所知的非常不发达的。主要创新点是：(A)基于最新图像处理技术的更显著的特征提取，如数学形态学、各种过滤器、小波、图论和概率分割等；(B)用于图像/文本融合和跨模式查询的新颖的基于图的方法和概率模型；(C)新的潜在空间模型，该模型捕捉不同形态背景下功能或行为相似的基因的更高级别的“语义相似性”，而不是直接的特征相似性；(D)卡尔曼滤波器和非线性动态模型，用于建模和预测基因表达的时空演化。此外，还将创建一个包含果蝇基因组中所有已知基因表达的公共来源的ISH图像库。结构化数据库可以按图像示例或关键字(全部自动派生)进行搜索。基因表达模式的原位杂交图像的系统化分析将引起人们的高度兴趣。需要强大而复杂的计算机算法来分析这些数据。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。这个项目是朝着最终的、长期的目标迈出的必要的一步，目的是了解胚胎发育的分子机制，以及解开这一过程中涉及的基因调控网络。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。

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.