Buch, Englisch, 621 Seiten, Format (B × H): 178 mm x 254 mm
Reihe: Methods in Molecular Biology
Buch, Englisch, 621 Seiten, Format (B × H): 178 mm x 254 mm
Reihe: Methods in Molecular Biology
ISBN: 978-1-0716-5566-5
Verlag: Springer Us
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
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,
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
LSTM-Based Methods for RNA-RNA Interaction Prediction.- Support Vector Machine (SVM)-Based Review of RNA-RNA Interaction Prediction Methods with Emphasis on Cancer Research and Treatment.- RRI-Senti: Transformer-Based Sentiment Analysis of Biomedical Literature for RNA-RNA Interaction Disease Insights.- Sentiment Analysis for RRI Disease Prediction Using RIScoper and Related Tools: A Comparative Review.- Machine Learning-Based Feature Set Analysis of RNA-RNA Interaction Dataset.- A Hybrid of LSTN-CNN for RNA-RNA Interaction Prediction.- A Comprehensive Comparison of the Deep Learning Approaches for the RNA-RNA Interaction Prediction Problem.- Comparative Analysis of Hyperparameter Optimization Strategies for RNA-RNA Interaction Prediction.- Pseudoknots: Based RRI Detection Using Machine Learning Models.- Evaluating Web Servers for RNA-RNA Interaction: From Feature Sets and Encodings to ML and DL Techniques.- Tutorial and Literature Survey on Machine Learning Methods for RNA-RNA Interaction Prediction: Naive Bayes, Bayesian Networks, and BERT Approaches.- From Object Detection to RNA Interaction: Evaluating YOLOv8 and CNNs on RRI Data.- Polyhedral Optimizations of RNA-RNA Interaction Predictions.- Genetic Algorithm-Based Solvers for RNA-RNA Interaction.- A Comprehensive Review of Machine Learning and Deep Learning Techniques for RRI Prediction.- Current Trends and Future Challenges in the RNA-RNA Interaction Prediction Tools and Methods.- Transformer-Based Architectures for RNA-RNA Interaction Prediction.- Graph Neural Networks for Structure-Aware RNA-RNA Interaction Modeling.- Multi-Omics Integration for RNA-RNA Interaction Prediction.- Explainable Artificial Intelligence for RNA-RNA Interaction Models.- Benchmarking, Reproducibility, and FAIR Pipelines for RNA-RNA Interaction Prediction.- RNA-RNA Interaction Prediction in Therapeutics and Drug Discovery.- Protocols for RNA-RNA Interaction Dataset Construction and Model Training.- Tutorial: End-to-End Deep Learning Pipeline for RNA-RNA Interaction Prediction.- Large Language Models in RNA-RNA Interaction Prediction.
