• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>NPG Open Access >Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes
【2h】

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes

机译：多个基因组特征的功能分析表明分类算法选择与表型相关的基因

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.
机译：毒性和临床反应的基因表达特征有利于安全性评估和临床实践；然而，将特征基因与预测的终点相连接的困难限制了它们的应用。微阵列质量控制协会II（MAQCII）项目从六个基因表达数据集中为十个临床和三个毒理学终点生成了262个签名，这是空前的各种签名集合，已允许对此类预测模型的性质进行广泛的分析。使用本体富集，生物网络构建和相互作用组连通性分析对这些签名及其非冗余结合的基因进行了全面分析，证明了基因签名与其预测能力的生物学基础之间的联系。给定终点的不同标记在生物学特性和转录控制水平上比在基因水平上更相似。签名倾向于以端点和特定于模型的方式丰富功能和途径，并显示传入交互的拓扑偏差。重要的是，对于给定的端点，不同签名之间的生物学相似性水平与签名预测的准确性呈正相关。这些发现将有助于对预测基因组特征的理解和应用，并支持其在预测医学中的广泛应用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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