ReviewComputational and structural biotechnology journal2024
Large language models and their applications in bioinformatics.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed.
- HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.RNA biology · 2026Article
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- Which chemical features are captured by ChemBERTa's attention?Journal of computer-aided molecular design · 2026Article
- GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.European radiology · 2026Article
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- Harnessing generative AI for predicting and optimizing antimicrobial peptides against drug-resistant infections.npj antimicrobials and resistance · 2026Article
- Protein and ligand novelty in drug-target interaction prediction: a dual-encoder fusion strategy for more interpretable and generalizable modeling.BMC bioinformatics · 2026Article
- AI-powered TargetMap: Enabling system-level target discovery through full-path reasoning on a unified knowledge graph.iScience · 2026Article
- Protein Large Language Models Can PredictACS omega · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- The sophist in the server : Rhetoric, Reasoning and Scientific Judgment in the Age of LLMs.EMBO reports · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- SummArIzeR: simplifying cross-database enrichment result clustering and annotation via large language models.Bioinformatics (Oxford, England) · 2026Article
- AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.Journal of translational medicine · 2026Review
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- AI in biocuration: challenges, opportunities, and a roadmap for sustainable integration.Bioinformatics advances · 2026Article
- Large Language Models for Non-Coding RNA Biomarker Discovery in Breast Cancer.Methods in molecular biology (Clifton, N.J.) · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in Natural Language Processing (NLP) have been significantly driven by the development of Large Language Models (LLMs), representing a substantial leap in language-based technology capabilities. These models, built on sophisticated deep learning architectures, typically transformers, are characterized by billions of parameters and extensive training data, enabling them to achieve high accuracy across various tasks. The transformer architecture of LLMs allows them to effectively handle context and sequential information, which is crucial for understanding and generating human language. Beyond traditional NLP applications, LLMs have shown significant promise in bioinformatics, transforming the field by addressing challenges associated with large and complex biological datasets. In genomics, proteomics, and personalized medicine, LLMs facilitate identifying patterns, predicting protein structures, or understanding genetic variations. This capability is crucial, e.g., for advancing drug discovery, where accurate prediction of molecular interactions is essential. This review discusses the current trends in LLMs research and their potential to revolutionize the field of bioinformatics and accelerate novel discoveries in the life sciences.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.