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In this chapter, I introduce machine learning approaches for predicting RNA–RNA/DNA interactions, which are crucial for understanding noncoding RNA (ncRNA) functions. Advancements in deep learning techniques and the availability of large-scale interaction data from high-throughput sequencing methods have driven the development of these prediction tools. This review covers representative studies across different RNA families, including prokaryotic small RNAs (TargetRNA3), general RNA–RNA interactions (CheRRI), miRNAs (DeepMirTar), box C/D snoRNAs (snoGloBe), lncRNA–DNA triplexes (triplexFPP), and CRISPR guide RNA design (CRISOT). These machine learning-based methods often improve accuracy compared to traditional energy-based approaches. However, there are challenges such as the need for preventing overfitting and third-party validation. Future advancements are expected to enhance the generalization and applicability of these prediction tools, contributing to a deeper understanding of RNA functions.

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Machine Learning Approaches for Predicting RNA–RNA/DNA Interactions

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  • 发表日期：

    2026年

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  • Tsukasa Fukunaga

摘要

In this chapter, I introduce machine learning approaches for predicting RNA–RNA/DNA interactions, which are crucial for understanding noncoding RNA (ncRNA) functions. Advancements in deep learning techniques and the availability of large-scale interaction data from high-throughput sequencing methods have driven the development of these prediction tools. This review covers representative studies across different RNA families, including prokaryotic small RNAs (TargetRNA3), general RNA–RNA interactions (CheRRI), miRNAs (DeepMirTar), box C/D snoRNAs (snoGloBe), lncRNA–DNA triplexes (triplexFPP), and CRISPR guide RNA design (CRISOT). These machine learning-based methods often improve accuracy compared to traditional energy-based approaches. However, there are challenges such as the need for preventing overfitting and third-party validation. Future advancements are expected to enhance the generalization and applicability of these prediction tools, contributing to a deeper understanding of RNA functions.