Application of dynamic topic models to toxicogenomics data
作者: Mikyung LeeZhichao LiuRuili HuangWeida Tong
作者单位: 1NIH/National Center for Advancing Translational Sciences
2National Center for Toxicological Research
刊名: BMC Bioinformatics, 2016, Vol.17 (13)
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-016-1225-0
关键词: Dynamic topic model (DTM)Times-series gene expressionToxicogenomicsTG-GATEsClusteringTopic modelingLatent Dirichlet model
原始语种摘要: Abstract(#br) Background(#br)All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data.(#br) Results(#br)A large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of...
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影响因子:3.024 (2012)

  • dynamic 动力学的
  • Application 应用
  • topic 话题