title: Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis.
abstract: 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.
authors: School of Plant Sciences, University of Arizona, Tucson, Arizona 85721-0036.
authors: Chuang
authors: C
authors: Ma
authors: Mingming
authors: M
authors: Xin
authors: Kenneth A
authors: KA
authors: Feldmann
authors: Xiangfeng
authors: X
authors: Wang
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000502:physiology*; D001185:Artificial Intelligence; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D017343:Genes, Plant; D056726:Genetic Association Studies; D010641:Phenotype; D015398:Signal Transduction / Q000187:drug effects / Q000235:genetics; D012965:Sodium Chloride / Q000494:pharmacology; D012984:Software; D013312:Stress, Physiological / Q000235:genetics*; D059467:Transcriptome / Q000235:genetics*
chemical_list: D012965:Sodium Chloride