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.
| Original language | English |
|---|---|
| Article number | 1770 |
| Journal | Frontiers in Plant Science |
| Volume | 871 |
| DOIs | |
| Publication status | Published - Jan 1 2018 |
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All Science Journal Classification (ASJC) codes
- Plant Science
Cite this
Statistical and machine learning approaches to predict gene regulatory networks from transcriptome datasets. / Mochida, Keiichi; Koda, Satoru; Inoue, Komaki; Nishii, Ryuei.
In: Frontiers in Plant Science, Vol. 871, 1770, 01.01.2018.Research output: Contribution to journal › Short survey
}
TY - JOUR
T1 - Statistical and machine learning approaches to predict gene regulatory networks from transcriptome datasets
AU - Mochida, Keiichi
AU - Koda, Satoru
AU - Inoue, Komaki
AU - Nishii, Ryuei
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85058781835&partnerID=8YFLogxK
U2 - 10.3389/fpls.2018.01770
DO - 10.3389/fpls.2018.01770
M3 - Short survey
AN - SCOPUS:85058781835
VL - 871
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
SN - 1664-462X
M1 - 1770
ER -