Computational Methods For Rna-Rna Interactions

This volume looks at the latest approaches for RNA–RNA interaction prediction, with particular emphasis on machine learning and deep learning methodologies. The chapters in this book provide a structured and in-depth exploration of computational methods for RNA–RNA interaction prediction and analysis, with focus on specific machine learning or deep learning technique and its application to RNA–RNA interactions (RRIs)-related problems. Some of the topics covered in this book include support vector machines, decision trees, and random forests; recurrent neural networks and graph neural networks; polyhedral modeling; evolutionary and genetic algorithms; machine learning versus deep learning approaches; and emerging trends in RNA–RNA interaction prediction, including multimodal data integration and model interpretability. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Comprehensive and authoritative, Computational Methods for RNA-RNA Interactions: Methods and Protocols integrates biological insight with computational rigor, and aims to support researchers seeking to understand, predict, and analyze RRIs in a systematic and practical manner. The content in this volume is designed for a broad audience, including molecular biologists interested in computational approaches, computer scientists and bioinformaticians entering RNA biology, and interdisciplinary researchers working at the interface of these fields.

This volume looks at the latest approaches for RNA–RNA interaction prediction, with particular emphasis on machine learning and deep learning methodologies. The chapters in this book provide a structured and in-depth exploration of computational methods for RNA–RNA interaction prediction and analysis, with focus on specific machine learning or deep learning technique and its application to RNA–RNA interactions (RRIs)-related problems. Some of the topics covered in this book include support vector machines, decision trees, and random forests; recurrent neural networks and graph neural networks; polyhedral modeling; evolutionary and genetic algorithms; machine learning versus deep learning approaches; and emerging trends in RNA–RNA interaction prediction, including multimodal data integration and model interpretability. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Comprehensive and authoritative, Computational Methods for RNA-RNA Interactions: Methods and Protocols integrates biological insight with computational rigor, and aims to support researchers seeking to understand, predict, and analyze RRIs in a systematic and practical manner. The content in this volume is designed for a broad audience, including molecular biologists interested in computational approaches, computer scientists and bioinformaticians entering RNA biology, and interdisciplinary researchers working at the interface of these fields.