ArticleInternational journal of molecular sciences2022
ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Non-coding RNAs as central regulators of phytohormone signaling in plant abiotic stress responses.Plant molecular biology · 2026Review
- Article
- Detecting the Type and Severity of Mineral Nutrient Deficiency in Rice Plants Based on an Intelligent microRNA Biosensing Platform.Sensors (Basel, Switzerland) · 2025Article
- Millets for a sustainable future.Journal of experimental botany · 2025Review
- Plant stress and proteomics in medicinal plants.Frontiers in plant science · 2025Review
- Machine Learning-Based Identification of Mating Type and Metalaxyl Response inMicroorganisms · 2024Article
- Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network.Plant methods · 2024Article
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics.Plant methods · 2023Article
- ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants.Functional & integrative genomics · 2023Article
- Genome-wide identification, characterization, evolution and expression analysis of theFrontiers in genetics · 2023Article
- Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.Biomedicines · 2022Article
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Authors and funding
9 authors.
Funding
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Abstract
MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo
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