Inference of gene interaction networks using conserved subsequential patterns from multiple time course gene expression datasets

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• 作者：Qian Liu ; Renhua Song ; Jinyan Li
• 关键词：gene interaction networks ; computational inference ; multiple time course gene expression datasets ; conserved subsequential patterns
• 刊名：BMC Genomics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：16
• 期：12-supp
• 全文大小：2,457 KB
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• 作者单位：Qian Liu (1)
  Renhua Song (1)
  Jinyan Li (2)
  
  1. Advanced Analytics Institute, University of Technology Sydney, Broadway, 2007, Sydney, Australia
  2. Advanced Analytics Institute and Centre for Health Technologies, University of Technology Sydney, Broadway, 2007, Sydney, Australia
  
• 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
• 出版者：BioMed Central
• ISSN：1471-2164

文摘

Motivation Deciphering gene interaction networks (GINs) from time-course gene expression (TCGx) data is highly valuable to understand gene behaviors (e.g., activation, inhibition, time-lagged causality) at the system level. Existing methods usually use a global or local proximity measure to infer GINs from a single dataset. As the noise contained in a single data set is hardly self-resolved, the results are sometimes not reliable. Also, these proximity measurements cannot handle the co-existence of the various in vivo positive, negative and time-lagged gene interactions.