Swetha Varadarajan

LSTM-Based Methods for RNA-RNA Interaction Prediction.

Evaluating Web Servers for RNA-RNA Interaction.

Protocols for RNA-RNA Interaction Dataset Construction and Model Training.

Multi-Omics Integration for RNA-RNA Interaction Prediction.

Comparative Analysis of Hyperparameter Optimization Strategies for RNA-RNA Interaction Prediction.

Current Trends and Future Challenges in the RNA-RNA Interaction Prediction Tools and Methods.

Transformer-Based Architectures for RNA-RNA Interaction Prediction.

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.

A Comprehensive Review of Machine Learning and Deep Learning Techniques for RRI Prediction.

Tutorial: End-to-End Deep Learning Pipeline for RNA-RNA Interaction Prediction.

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.

Pseudoknots: Based RRI Detection Using Machine Learning Models.

A Hybrid of LSTM-CNN for RNA-RNA Interaction Prediction.

Tutorial on Machine Learning Methods for RNA-RNA Interaction Prediction.

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...