AUTHOR=Mochida Keiichi , Koda Satoru , Inoue Komaki , Nishii Ryuei TITLE=Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets JOURNAL=Frontiers in Plant Science VOLUME=9 YEAR=2018 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.01770 DOI=10.3389/fpls.2018.01770 ISSN=1664-462X ABSTRACT=

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.