Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis

Plant Cell. 2014 Feb;26(2):520-37. doi: 10.1105/tpc.113.121913. Epub 2014 Feb 11.


Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Arabidopsis / genetics*
  • Arabidopsis / physiology*
  • Artificial Intelligence*
  • Databases, Genetic
  • Gene Expression Profiling*
  • Gene Expression Regulation, Plant
  • Gene Regulatory Networks*
  • Genes, Plant
  • Genetic Association Studies
  • Phenotype
  • Signal Transduction / drug effects
  • Signal Transduction / genetics
  • Sodium Chloride / pharmacology
  • Software
  • Stress, Physiological / genetics*
  • Transcriptome / genetics*


  • Sodium Chloride