ReviewBiosafety and health2025
Large language models for biological sequence analysis in infectious disease research.
Review in Biosafety and health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Computational biology and bioinformatics for infectious disease research: From molecular mechanisms to population-level surveillance.Biosafety and health · 2026Article
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have emerged as transformative tools in infectious disease research, offering unprecedented capabilities in analyzing biological sequences. This review summarizes three primary types of biological LLMs, including protein language models, genomic language models, and multimodal models, highlighting their architectures and applications. These models are revolutionizing key areas such as pathogen identification, evolutionary surveillance, host-pathogen prediction, and therapeutic development by enabling the interpretation of complex genomic and proteomic data at an unparalleled scale. While recent advancements are remarkable, challenges persist in data quality, long-context processing, model interpretability, and biosafety considerations. Understanding the potential and limitations of LLMs is crucial for leveraging them effectively in infectious disease research while ensuring responsible development and deployment.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.