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Protein Function Prediction: Methods and Protocols Second Edition 2025 [Hardback]

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  • Formāts: Hardback, 340 pages, height x width: 254x178 mm, 76 Illustrations, color; 2 Illustrations, black and white; X, 340 p. 78 illus., 76 illus. in color., 1 Hardback
  • Sērija : Methods in Molecular Biology 2947
  • Izdošanas datums: 08-Sep-2025
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1071646613
  • ISBN-13: 9781071646618
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Protein Function Prediction: Methods and Protocols Second Edition 2025Palielināt
  • Formāts: Hardback, 340 pages, height x width: 254x178 mm, 76 Illustrations, color; 2 Illustrations, black and white; X, 340 p. 78 illus., 76 illus. in color., 1 Hardback
  • Sērija : Methods in Molecular Biology 2947
  • Izdošanas datums: 08-Sep-2025
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1071646613
  • ISBN-13: 9781071646618
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781071646618.html

This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational techniques, such as machine learning, multi-task learning, protein language models, and deep learning, the book continues by covering specific tools for protein function prediction, ranging from gene ontology-term predictions to the predictions of binding sites, protein localization and solubility, signal peptides, intrinsic disorder, and intrinsically disordered binding regions, as well as presenting databases that address protein moonlighting and protein binding. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, step-by-step instructions on how to use software and web resources, use cases, and tips on troubleshooting and avoiding known pitfalls.

 

Authoritative and up-to-date, Protein Function Prediction: Methods and Protocols, Second Edition helps readers to understand and appreciate this vibrant and growing research area and guides in the quest to identify and use the best computational methods and resources for their projects.

Computational Prediction of Protein Functional Annotations.- Machine
Learning for Protein Function Prediction.- Graph Neural Network-Based
Approaches for Protein Function Prediction.- Multi-Task Learning-Based
Approaches for Protein Function Prediction.- A Survey of Deep Learning
Methods and Tools for Protein Binding Site Prediction.- A Survey of Current
Status in AI-Based Topology Prediction of Transmembrane Proteins.- NetGO 3.0:
A Recent Protein Function Prediction Tool Based on Protein Language Model.-
Predicting Protein Functions with Function-Aware Domain Embeddings Using
Domain-PFP.- Integrating Gene Ontology Relationships for Protein Function
Prediction Using PFresGO.- Annotating Genomes with DeepGO Protein Function
Prediction Tools.- An Online Server for Geometry-Aware Protein Function
Annotations through Predicted Structure.- Exploring Binding Sites on Proteins
for Function Prediction Using the PoSSuM Databases.- Comprehensive Prediction
of Protein Localization and Signal Peptides Using MULocDeep.- A Benchmarking
Platform for Assessing Protein Language Models on Function-Related Prediction
Tasks.- Prediction of Intrinsic Disorder Functions with DEPICTER2.-
Prediction of Disordered Linear Interacting Peptides with CLIP.- Prediction
of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.- NaviGO:
An Interactive Tool for Gene Ontology Functional Analysis with Free Text GO
Summaries.- Using the MoonProt Database for Understanding Protein Functions.-
Illustrative Features and Utilities of MPAD: Thermodynamic Database for
Membrane Protein-Protein Complexes.