Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets
書誌事項
- 公開日
- 2018-11-29
- 資源種別
- journal article
- 権利情報
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- https://creativecommons.org/licenses/by/4.0/
- DOI
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- 10.3389/fpls.2018.01770
- 公開者
- Frontiers Media SA
説明
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.
収録刊行物
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- Frontiers in Plant Science
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Frontiers in Plant Science 9 1770-, 2018-11-29
Frontiers Media SA
