Evidence map›Paper›PMID 42108304›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

RNA Large Language Models in Virology.

Weiwei Shen, Liucun Zhu, Yixue Li, Tao Huang

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Weiwei ShenSchool of Life Sciences, Shanghai University, Shanghai, China.
Liucun ZhuSchool of Life Sciences, Shanghai University, Shanghai, China.
Yixue LiDepartment of Artificial Intelligence and Digital Health, CAS engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. li_yixue@gzlab.ac.cn.
Tao HuangDepartment of Artificial Intelligence and Digital Health, CAS engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. huangtao@sinh.ac.cn.ORCID https://orcid.org/0000-0003-1975-9693

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The viral gene sequence is regarded as a kind of special text, which constitutes a core innovation in the application of large language models to virus research. This idea breaks free from the shackles of the traditional analysis framework and opens up a new way of computing to understand the evolution of the virus. Using this method, researchers can quickly and accurately locate the key mutation sites of the virus. This helps to detect emerging variant strains in time and assess their potential impact on transmissibility and pathogenicity. Especially importantly, this technology can also reconstruct the evolutionary path of the virus and realize real-time tracking of the mutation process. This provides a powerful tool for a systematic understanding of the adaptive evolution of the virus. These profound insights based on the big language model can be used as a theoretical basis for the development of targeted vaccines and drugs. Although the relevant computing technology is in the development stage, it has shown great potential to promote the transformation of virological research to a predictable direction.

Indexed as

Large Language ModelsRNA, ViralVirologyEvolution, MolecularGenome, ViralHumansMutationVirusesRNA, ViralArtificial intelligenceGenomicsLarge language models (LLMs)Virology

Identifiers

What OpenQuestion holds

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Registered trials

None linked

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.