Description

Book Synopsis

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.

Protein Function Prediction

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      Publisher: Springer-Verlag New York Inc.
      Publication Date: Publication Date: 18/09/2025
      ISBN13: 9781071646618, 978-1071646618
      ISBN10:
      Also in:
      Biology, life sciences

      Description

      Book Synopsis

      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.

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