Statistical and machine learning approaches to predict gene regulatory networks from transcriptome datasets

Keiichi Mochida, Satoru Koda, Komaki Inoue, Ryuei Nishii

Research output: Contribution to journalShort surveypeer-review

43 Citations (Scopus)


Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.

Original languageEnglish
Article number1770
JournalFrontiers in Plant Science
Publication statusPublished - 2018


  • Gene regulatory network
  • Machine learning
  • Sparse modeling
  • Time series analysis
  • Transcriptome

ASJC Scopus subject areas

  • Plant Science


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