Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets
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, thereb...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-11-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fpls.2018.01770/full |
| _version_ | 1828790717266264064 |
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| author | Keiichi Mochida Keiichi Mochida Keiichi Mochida Keiichi Mochida Satoru Koda Komaki Inoue Ryuei Nishii |
| author_facet | Keiichi Mochida Keiichi Mochida Keiichi Mochida Keiichi Mochida Satoru Koda Komaki Inoue Ryuei Nishii |
| author_sort | Keiichi Mochida |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T01:35:28Z |
| format | Article |
| id | doaj.art-7d63abcaad9741d492d2aadb1bf0ae3f |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-12T01:35:28Z |
| publishDate | 2018-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-7d63abcaad9741d492d2aadb1bf0ae3f2022-12-22T00:42:51ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2018-11-01910.3389/fpls.2018.01770421043Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome DatasetsKeiichi Mochida0Keiichi Mochida1Keiichi Mochida2Keiichi Mochida3Satoru Koda4Komaki Inoue5Ryuei Nishii6Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, JapanMicroalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, JapanInstitute of Plant Science and Resources, Okayama University, Kurashiki, JapanKihara Institute for Biological Research, Yokohama City University, Yokohama, JapanGraduate School of Mathematics, Kyushu University, Fukuoka, JapanBioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, JapanInstitute of Mathematics for Industry, Kyushu University, Fukuoka, JapanStatistical 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.https://www.frontiersin.org/article/10.3389/fpls.2018.01770/fullmachine learninggene regulatory networksparse modelingtranscriptometime series analysis |
| spellingShingle | Keiichi Mochida Keiichi Mochida Keiichi Mochida Keiichi Mochida Satoru Koda Komaki Inoue Ryuei Nishii Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets Frontiers in Plant Science machine learning gene regulatory network sparse modeling transcriptome time series analysis |
| title | Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets |
| title_full | Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets |
| title_fullStr | Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets |
| title_full_unstemmed | Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets |
| title_short | Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets |
| title_sort | statistical and machine learning approaches to predict gene regulatory networks from transcriptome datasets |
| topic | machine learning gene regulatory network sparse modeling transcriptome time series analysis |
| url | https://www.frontiersin.org/article/10.3389/fpls.2018.01770/full |
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