Abstract
In addition to encoding proteins, mRNAs have context-specific regulatory roles that contribute to many cellular processes. However, uncovering new mRNA functions is constrained by limitations of traditional biochemical and computational methods. In this Roadmap, we highlight how artificial intelligence can transform our understanding of RNA biology by fostering collaborations between RNA biologists and computational scientists to drive innovation in this fundamental field of research. We discuss how non-coding regions of the mRNA, including introns and 5′ and 3′ untranslated regions, regulate the metabolism and interactomes of mRNA, and the current challenges in characterizing these regions. We further discuss large language models, which can be used to learn biologically meaningful RNA sequence representations. We also provide a detailed roadmap for integrating large language models with graph neural networks to harness publicly available sequencing and knowledge data. Adopting this roadmap will allow us to predict RNA interactions with diverse molecules and the modelling of context-specific mRNA interactomes.
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Acknowledgements
This work is supported by the Swiss National Science Foundation (310030_207907 to Z.M.X.; 215906 to C.T. and J.H.; 1000144 to C.V.C.; and 205121_207437 to L.V.P.). A.R. and M.D. are supported by Wellcome Trust Investigator Award (217213/Z/19/Z), Y.W. holds a Non-Clinical Junior Research Fellowship from the Motor Neurone Disease Association (Wang/Oct23/2324-799), and R.P. holds a Lister Research Prize Fellowship.
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Glossary
- Attention maps
-
Matrix of attention coefficients, often used in the context of self-attention in Transformers. A large attention coefficient between two tokens means that one of them will weigh heavily in the updated representation of the other.
- Contrastive learning
-
Machine learning technique in which a model learns to differentiate between similar and dissimilar pairs of data by bringing similar pairs closer in the representation space and pushing dissimilar pairs apart.
- Embeddings
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Learned, vectorial abstract representations of data.
- Fine-tuning
-
Fine-tuning is a secondary training stage of foundation models or models that are being trained in multiple stages, usually done on smaller, labelled datasets to specialize the model on a given task.
- Foundation model
-
A model trained on a large quantity of data to be general purpose and suitable for a variety of predictive tasks for a given data modality, in contrast to task-specific models.
- Graph neural networks
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(GNNs). Deep learning models dedicated to learning on graphs.
- Heterogeneous
-
Graphs are homogeneous when their nodes and edges share comparable features regardless of entity or interaction type. If node or edge features originate from different spaces, the graph is heterogeneous.
- Hierarchical training
-
A machine learning strategy in which a deep learning model, composed of a succession of components dedicated to the encoding of specific modalities or concepts, is trained to achieve a given final task. Each intermediate component can be associated with a specific objective function to optimize.
- Knowledge graphs
-
Graph-structured representations of knowledge about a domain, typically comprising one or more ontologies associated to instance data of various types and their inter-relationships.
- Knowledge-based weighting strategies
-
Strategies in machine learning in which weights are assigned to training data points based on their biological or domain-specific importance rather than treating all data equally. These strategies help models learn from scarce but crucial data, which is particularly beneficial for training with imbalanced datasets.
- Large language models
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Deep learning models that are trained in a self-supervised way on sequences. They often feature stacked transformer layers, resulting in millions to billions of parameters.
- Message passing algorithms
-
A computational method used in graph-based models, where each node exchanges information with other specific nodes in the graph. These nodes can be direct or indirect neighbours of the central node. This process is used in GNNs to iteratively update the representation of each node according to the structure of the graph and the learned representations of neighbouring nodes.
- Multimodal alignment
-
The use of multiple modalities that do not represent the same object but are related in some way.
- Multimodal fusion
-
The process of combining multiple modalities representing the same object, for example, the sequence, structure and functional annotation of a given RNA sequence.
- Multitask training
-
A machine learning strategy in which a model is trained to perform multiple tasks simultaneously. The model shares a common underlying architecture for all tasks, but has specific outputs tailored to each task.
- Objective function
-
The function that is being optimized (minimized or maximized) during training, typically a loss function that represents the difference between what the model predicts and the ground truth.
- Ontology
-
A knowledge representation structure in which explicit knowledge about a topic is organized into classes and relationships, each of which is given a definition and associated synonyms and other metadata.
- Pretraining
-
Generally used in the context of foundation models or other transfer learning scenarios, it is the first training step that allows the model to build general-purpose representations. It is generally done on large datasets, often on unlabelled data and training is done in a self-supervised manner.
- Self-attention mechanisms
-
Flexible mechanisms for a transformer model to learn the relative context-specific relationships between input tokens. They are the attention mechanisms that calculate attention scores between all elements of the input, and output the weighted averages using those scores.
- Self-supervised learning
-
A paradigm in machine learning in which a model learns the structure of a dataset from the dataset itself without additional explicit labels, for example, by being tasked to learn to predict masked or missing parts of the data given surrounding elements.
- Semantic loss function
-
A type of loss function used in machine learning models to ensure that the predictions made by the model adhere to specific domain knowledge or logical constraints, in addition to minimizing the prediction error.
- Supervised deep learning
-
A machine learning paradigm in which an input object (for example, a sequence), together with a desired output value (for example, the type of sequence), are used to train a predictive model capable of inferring outputs of this type for new inputs.
- Tokenization
-
The process of splitting a sequence into defined subunits, called ‘tokens’. In RNA, sequences are often split into nucleotides or in overlapping k-mers.
- Transfer learning
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A paradigm in machine learning in which a model is trained in different stages, with the learned parameters from earlier stages in the training being retained or ‘transferred’ into later stages, where they benefit downstream predictions.
- Transformer architecture
-
A popular and powerful deep learning architecture that is characterized by an attention mechanism and positional encodings that allow complex contextual relationships to be learned. The architecture is made up of transformer blocks of a linear transformation layer, followed by a batch normalization layer, followed by a self-attention layer and one last batch normalization layer.
- Vectorized representations
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Abstract, numerical representation of data in the form of a vector of (usually) continuous values.
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Jung, V., Vincent-Cuaz, C., Tumescheit, C. et al. Decoding the interactions and functions of non-coding RNA with artificial intelligence. Nat Rev Mol Cell Biol 26, 797–818 (2025). https://doi.org/10.1038/s41580-025-00857-w
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DOI: https://doi.org/10.1038/s41580-025-00857-w
