AUTOMATED ANOMALY DETECTIONS IN CYTOGENETICS

该项目的目标是开发一个显著改进的 用于染色体异常检测的软件，通过表征 染色体带型，显示其基本特征 特征，建立理想化的表示， 特定类型，并定量比较染色体， 原型和彼此。 这将提高特异性 染色体带型特征，特别是高 分辨率条带，以检测和量化亚可见光 带型改变 具体来说，我们将开发染色体图像分析软件 这将（1）提取带模式相似性的本质 （2）显示一组染色体的理想化图像， 它们共有的特征，（3）染色体分类 通过将每一个与每一种同系物类型的原型进行比较，（4） 相互比较同源物以检测细微的异常，（5） 比较染色体片段以检测异常，（6）开发 典型的染色体图像， 基因相关疾病并与正常人进行比较。 我们 将使用特征向量方法分解染色体图像 分为组件，选择那些组件，反映了 的相关特征，同时消除 反映预备伪影的成分，染色体间 差异和其他不相关的差异。 此新功能 将大大提高自动染色体的能力， 分析仪器，以评估染色体改变， 肿瘤和正常哺乳动物细胞。

The goal of this project is to develop a significantly improved software for chromosome anomaly detection, by characterizing a chromosome banding pattern, displaying its essential characteristics, establishing idealized representations for specific types, and quantitatively comparing chromosomes to prototypes and to each other. This will improve the specificity of chromosome band pattern characterization, particularly with high resolution banding, in order to detect and quantify sub-visible band pattern alterations. Specifically, we will develop chromosome image analysis software that will (1) extract the essence of the band pattern similarities of a group of chromosomes, (2) display an idealized image showing the characteristics they have in common, (3) classify chromosomes by comparing each to the prototype for each homolog type, (4) compare homologs to each other to detect subtle abnormalities, (5) compare segments of chromosomes to detect anomalies, (6) develop prototypical chromosome images for groups of people manifesting genetically linked diseases and compare these with normals. We will use the Eigenvector method of decomposing chromosome images into components, selecting those components which reflect the relevant characteristics of the banding pattern, while eliminating components that reflect preparatory artefact, inter-chromosome variation, and other irrelevant differences. This new capability will significantly increase the ability of automated chromosome analysis instruments to evaluate chromosome alterations in neoplastic and in normal mammalian cells.