Large Language Models (LLMs) in Protein Bioinformatics (eBook, PDF)

Redaktion: Kc, Dukka B.

• Format: PDF

Produktbeschreibung

This book presents a comprehensive collection of methods, resources, and studies that use large language models (LLMs) in the field of protein bioinformatics. Reflecting the swift pace of LLM development today, the volume delves into numerous LLM-based tools to investigate proteins science, from protein language models to the prediction of protein-ligand binding sites. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice to ensure success in future research.

Authoritative and practical, Large Language Models (LLMs) in Protein Bioinformatics serves as an ideal guide for scientists seeking to tap into the potential of artificial intelligence in this vital area of biological study.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 358
• Erscheinungstermin: 2. Juli 2025
• Englisch
• ISBN-13: 9781071646236
• Artikelnr.: 74797567

Inhaltsangabe

A Survey of Pre-Trained Protein Language Models.- Enhancing Structure-Aware Protein Language Models with Efficient Fine-Tuning for Various Protein Prediction Tasks.- Exploring ProtFlash: An Efficient Approach to Protein Data Analysis.- Ranking Protein-Protein Models with Large Language Models and Graph Neural Networks.- Translating a GO Term List to Human Readable Function Description Using GO2Sum.- TransFun: A Tool of Integrating Large Language Models, Transformers, and Equivariant Graph Neural Networks to Predict Protein Function.- Using InterLabelGO+ for Accurate Protein Language Model-Based Function Prediction.- Functional Annotation of Proteomes Using Protein Language Models: A High-Throughput Implementation of the ProtTrans Model.- Advances in Language-Model-Informed Protein-Nucleic Acid Binding Site Prediction.- Practical Applications of Language Models in Protein Sorting Prediction: SignalP 6.0, DeepLoc 2.1, and DeepLocPro 1.0.- CNN-Meth: A Tool to Accurately Predict Lysine Methylation Sites Using Evolutionary Information-Based Protein Modeling.- Predicting the Pathogenicity of Human Protein Variants: Not Only a Matter of Residue Labeling.- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).- PLMSearch and PLMAlign: Protein Language Model-Based Homologous Sequence Search and Alignment.- Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein-Protein Interaction Analysis.- Prediction of Protein-Peptide Binding Sites Using PepBCL.- Predicting Peptide Bioactivity Using the Unified Model Architecture UniDL4BioPep.- CLAPE: Protein-Ligand Binding Site Prediction via Protein Language Models.- Large Language Model-Based Advances in Prediction of Post-Translational Modification Sites in Proteins.