2027· Methods in molecular biology· Vol 3079, pp.
153-162
· 0 citations
Medicine
TL;DR
This tutorial and literature survey provide a comprehensive overview of machine learning methodologies applied to RRI prediction, focusing on classical models such as Naive Bayes and Bayesian networks, alongside cutting-edge deep learning techniques exemplified by Bidirectional Encoder Representations from Transformers (BERT).
This chapter provides an end-to-end guide for constructing deep learning pipelines for RNA-RNA interaction (RRI) prediction, starting from raw RNA sequences, and covers dataset curation, negative sampling, feature engineering, model selection, training, evaluation, and interpretability using explainable AI techniques.
S. Harithanush, P. Chokalingam, Swetha Varadarajan· Methods in molecular biology· 0 citations
This chapter provides a comprehensive overview of Transformer-based models for RRI prediction, including RNABERT and biomedical adaptations of BERT, discusses RNA-specific tokenization and pretraining strategies, and examines fine-tuning approaches for interaction prediction tasks.
Akhilesh Ramachandran, Trilok Sai Vaddanam, Swetha Varadarajan· Methods in molecular biology· 0 citations
