Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Keiichi Mochida, Satoru Koda, Komaki Inoue, Ryuei Nishii
2018 Frontiers in Plant Science  
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
more » ... fer 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.
doi:10.3389/fpls.2018.01770 pmid:30555503 pmcid:PMC6281826 fatcat:phaqyh5odvf4nkcwrwiqstoz2i