• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International symposium on integrative bioinformatics, 8th annual meeting >Integrated Simultaneous Analysis of Different Biomedical Data Types with Exact Weighted Bi-cluster Editing
【24h】

Integrated Simultaneous Analysis of Different Biomedical Data Types with Exact Weighted Bi-cluster Editing

机译：精确加权双聚类编辑对不同生物医学数据类型进行综合同时分析

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

The explosion of biological data has largely influenced the focus of today's biology research. Integrating and analysing large quantity of data to provide meaningful insights has become the main challenge to biologists and bioinformaticians. One major problem is the combined data analysis of data from different types, such as phenotypes and genotypes. This data is modelled as bi-partite graphs where nodes correspond to the different data points, mutations and diseases for instance, and weighted edges relate to associations between them. Biclustering is a special case of clustering designed for partitioning two different types of data simultaneously. We present a bi-clustering approach that solves the NP-hard weighted bi-cluster editing problem by transforming a given bi-partite graph into a disjoint union of bi-cliques. Here we contribute with an exact algorithm that is based on fixedparameter tractability. We evaluated its performance on artificial graphs first. Afterwards we exemplarily applied our Java implementation to data of genomewide association studies (GWAS) data aiming for discovering new, previously unobserved geno-to-pheno associations. We believe that our results will serve as guidelines for further wet lab investigations. Generally our software can be applied to any kind of data that can be modelled as bi-partite graphs. To our knowledge it is the fastest exact method for weighted bi-cluster editing problem.
机译：生物数据的爆炸极大地影响了当今生物学研究的重点。集成和分析大量数据以提供有意义的见解已成为生物学家和生物信息学家的主要挑战。一个主要问题是对来自不同类型（例如表型和基因型）的数据进行组合数据分析。此数据建模为二部图，其中节点对应于不同的数据点，例如突变和疾病，而加权边与它们之间的关联有关。 Biclustering是群集的一种特殊情况，旨在同时对两种不同类型的数据进行分区。我们提出了一种双聚类方法，该方法通过将给定的二部图转换成双斜体的不相交并集来解决NP硬加权双聚类编辑问题。在这里，我们为基于固定参数易处理性的精确算法做出了贡献。我们首先在人工图上评估了它的性能。之后，我们示例性地将Java实现应用于全基因组关联研究（GWAS）数据，旨在发现新的，以前未发现的基因对现象的关联。我们相信我们的结果将为进一步的湿实验室研究提供指导。通常，我们的软件可以应用于可以建模为二部图的任何类型的数据。据我们所知，这是加权双群集编辑问题的最快的准确方法。

著录项

  • 来源
  • 会议地点 Hangzhou(CN)
  • 作者

    Peng Sun; Jiong Guo; Jan Baumbach;

  • 作者单位

    Computational Systems Biology group, Max Planck Institute for Informatics, Campus E1. 4,66123 Saarbrucken, Germany Cluster of Excellence for Multimodal Computing and Interaction, Saarland University,Campus E1.7, 66123 Saarbrucken, Germany Saarland University, Campus E1.7, 66123 Saarbrucken, Germany;

    Cluster of Excellence for Multimodal Computing and Interaction, Saarland University,Campus E1.7, 66123 Saarbrucken, Germany Saarland University, Campus E1.7, 66123 Saarbrucken, Germany;

    Computational Systems Biology group, Max Planck Institute for Informatics, Campus E1. 4,66123 Saarbrucken, Germany Cluster of Excellence for Multimodal Computing and Interaction, Saarland University,Campus E1.7, 66123 Saarbrucken, Germany;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物信息论;
  • 关键词

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