Deciphering plant transcriptomes: Leveraging machine learning for deeper insights
Plant transcriptomics is an important field for understanding the dynamics of gene expression, regulatory mechanisms and interactions underlying plant development and stress responses. Despite advances in high-throughput sequencing technologies, the vast amount of transcriptomic data poses significa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Current Plant Biology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214662824001142 |
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| author | Bahman Panahi Rasmieh Hamid Hossein Mohammad Zadeh Jalaly |
| author_facet | Bahman Panahi Rasmieh Hamid Hossein Mohammad Zadeh Jalaly |
| author_sort | Bahman Panahi |
| collection | DOAJ |
| description | Plant transcriptomics is an important field for understanding the dynamics of gene expression, regulatory mechanisms and interactions underlying plant development and stress responses. Despite advances in high-throughput sequencing technologies, the vast amount of transcriptomic data poses significant challenges to traditional methods of analysis and limits the generation of meaningful biological insights. This review addresses the integration of machine learning (ML) techniques in plant transcriptomics and emphasizes their potential to transform data analysis and interpretation. We analyzed different ML methods and their applications in the identification of differentially expressed genes (DEGs), the elucidation of functional annotations and the reconstruction of regulatory networks. The main results show that ML approaches improve the accuracy of transcriptome analyses and facilitate the identification of novel gene functions and regulatory interactions that may be overlooked by conventional methods. The implications of this work are profound. The use of ML can lead to a deeper understanding of plant biology and significantly impact crop improvement strategies. By revealing the complexity of stress tolerance and developmental processes, ML applications can inform breeding programs and improve agricultural resilience. Future research should focus on refining ML algorithms, improving the accessibility of these tools for plant scientists, and fostering interdisciplinary collaborations to maximize the potential of ML in plant transcriptomics. |
| format | Article |
| id | doaj-art-1ec219634d0f4b97a8c0f179ff618c4d |
| institution | OA Journals |
| issn | 2214-6628 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Plant Biology |
| spelling | doaj-art-1ec219634d0f4b97a8c0f179ff618c4d2025-08-20T02:13:45ZengElsevierCurrent Plant Biology2214-66282025-03-014110043210.1016/j.cpb.2024.100432Deciphering plant transcriptomes: Leveraging machine learning for deeper insightsBahman Panahi0Rasmieh Hamid1Hossein Mohammad Zadeh Jalaly2Department of Genomics, Branch for Northwest & West region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran; Corresponding authors.Department of Plant Breeding, Cotton Research Institute of Iran (CRII), Agricultural Research, Education and Extension Organization (AREEO), Gorgan, Iran; Corresponding authors.Department of Genomics, Branch for Northwest & West region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, IranPlant transcriptomics is an important field for understanding the dynamics of gene expression, regulatory mechanisms and interactions underlying plant development and stress responses. Despite advances in high-throughput sequencing technologies, the vast amount of transcriptomic data poses significant challenges to traditional methods of analysis and limits the generation of meaningful biological insights. This review addresses the integration of machine learning (ML) techniques in plant transcriptomics and emphasizes their potential to transform data analysis and interpretation. We analyzed different ML methods and their applications in the identification of differentially expressed genes (DEGs), the elucidation of functional annotations and the reconstruction of regulatory networks. The main results show that ML approaches improve the accuracy of transcriptome analyses and facilitate the identification of novel gene functions and regulatory interactions that may be overlooked by conventional methods. The implications of this work are profound. The use of ML can lead to a deeper understanding of plant biology and significantly impact crop improvement strategies. By revealing the complexity of stress tolerance and developmental processes, ML applications can inform breeding programs and improve agricultural resilience. Future research should focus on refining ML algorithms, improving the accessibility of these tools for plant scientists, and fostering interdisciplinary collaborations to maximize the potential of ML in plant transcriptomics.http://www.sciencedirect.com/science/article/pii/S2214662824001142MLPlant transcriptomicsStress toleranceDevelopmental processesCrop improvement |
| spellingShingle | Bahman Panahi Rasmieh Hamid Hossein Mohammad Zadeh Jalaly Deciphering plant transcriptomes: Leveraging machine learning for deeper insights Current Plant Biology ML Plant transcriptomics Stress tolerance Developmental processes Crop improvement |
| title | Deciphering plant transcriptomes: Leveraging machine learning for deeper insights |
| title_full | Deciphering plant transcriptomes: Leveraging machine learning for deeper insights |
| title_fullStr | Deciphering plant transcriptomes: Leveraging machine learning for deeper insights |
| title_full_unstemmed | Deciphering plant transcriptomes: Leveraging machine learning for deeper insights |
| title_short | Deciphering plant transcriptomes: Leveraging machine learning for deeper insights |
| title_sort | deciphering plant transcriptomes leveraging machine learning for deeper insights |
| topic | ML Plant transcriptomics Stress tolerance Developmental processes Crop improvement |
| url | http://www.sciencedirect.com/science/article/pii/S2214662824001142 |
| work_keys_str_mv | AT bahmanpanahi decipheringplanttranscriptomesleveragingmachinelearningfordeeperinsights AT rasmiehhamid decipheringplanttranscriptomesleveragingmachinelearningfordeeperinsights AT hosseinmohammadzadehjalaly decipheringplanttranscriptomesleveragingmachinelearningfordeeperinsights |