Description
Book SynopsisComputational 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.
