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  # PlantRNA-FM: An Interpretable RNA Foundation Model for Exploration Functional RNA Motifs in Plants
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  ## Introduction
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  In the dynamic field of life sciences, the exploration of RNA as a fundamental element in biological processes has led to significant scientific advancements. RNA molecules, characterized by complex sequences and structures, play critical roles in plant growth, development, and adaptation to environmental changes. Recent developments in artificial intelligence, specifically foundation models (FMs), have opened new frontiers for understanding and harnessing this complexity. Building on this momentum, we introduce PlantRNA-FM, a state-of-the-art RNA foundation model tailored for plants. This model integrates both RNA sequence and structural data from an extensive compilation of plant species, enabling unprecedented accuracy in predicting RNA functions and understanding translation dynamics. By combining robust pre-training on diverse RNA data with sophisticated interpretative frameworks, PlantRNA-FM sets a new standard in RNA bioinformatics, providing deep insights into the functional significance of RNA motifs within the plant transcriptome.
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  # PlantRNA-FM: An Interpretable RNA Foundation Model for Exploration Functional RNA Motifs in Plants
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+ ### OmniGenome
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+ We have release a new model trained on the OneKP database, a.k.a., [OmniGenome](https://huggingface.co/yangheng/OmniGenome-186M).
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+ This model achieves updated results on various downstream tasks. Feel free to try it and leave feedbacks!
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  ## Introduction
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  In the dynamic field of life sciences, the exploration of RNA as a fundamental element in biological processes has led to significant scientific advancements. RNA molecules, characterized by complex sequences and structures, play critical roles in plant growth, development, and adaptation to environmental changes. Recent developments in artificial intelligence, specifically foundation models (FMs), have opened new frontiers for understanding and harnessing this complexity. Building on this momentum, we introduce PlantRNA-FM, a state-of-the-art RNA foundation model tailored for plants. This model integrates both RNA sequence and structural data from an extensive compilation of plant species, enabling unprecedented accuracy in predicting RNA functions and understanding translation dynamics. By combining robust pre-training on diverse RNA data with sophisticated interpretative frameworks, PlantRNA-FM sets a new standard in RNA bioinformatics, providing deep insights into the functional significance of RNA motifs within the plant transcriptome.
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