• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data
【24h】

Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data

机译：Hyppo-x：一个可扩展的探索框架，用于分析复杂的表情数据

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.
机译：表情是现代生物学的新兴分支，使用高吞吐量表型工具来捕获多种环境和表型特征，通常在大规模的空间和时间尺度。由此产生的高尺寸数据表示信息的资料，以便深入了解多种因素如何相互作用和有助于不同基因型的整体生长和行为。然而，目前缺乏可以通过如此复杂数据解析的计算工具并辅助提取合理的假设。在本文中，我们呈现Hyppo-X，一种新的算法方法来视觉上探索复杂的表情数据，并在过程中表征环境对表型性状的作用。我们将问题建模为一个无监督的结构发现之一，并使用来自代数拓扑结构和图表理论的新兴原理来发现复杂表情数据的高阶结构。我们提出了一个开源软件，具有交互式可视化功能，以促进数据导航和假设制定。我们在两个现实世界植物（玉米）数据集上测试和评估Hyppo-x。我们的结果展示了我们对不同亚贫民水平行为描绘的方法的能力。值得注意的是，我们的方法展示了环境因素如何影响表型行为，以及这种效果如何在不同的基因型和不同的时间尺度上变化。据我们所知，这项努力提供了第一种系统地将假设提取问题进行了系统正式的方法，以便对表达数据进行虚拟提取问题。考虑到表达领域的婴儿，帮助用户以数据引导方式探索复杂数据和提取合理假设的工具对未来使用此类数据的未来进步至关重要。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号