ArticleMethods in molecular biology (Clifton, N.J.)2025
Generative Artificial Intelligence for Virology.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Animal virus-host interactions mediated by non-coding RNAs.Frontiers in cellular and infection microbiology · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID crisis has accelerated the integration of artificial intelligence (AI) in drug discovery and omics research, providing novel avenues to tackle intricate issues in virology research. AI has lately enabled significant breakthroughs in a wide range of biological disciplines, including genetic variant interpretation, protein structure prediction, disease detection, and pharmaceutical creation. It has prominently assumed a pivotal role in virology research, with generative AI at the forefront of innovation. Generative AI (GAI) is a subset of AI that majorly focuses on creating new data or content resembling existing data through learning underlying patterns and relationships. It has revolutionized virology/omics study by generating synthetic data to augment limited datasets, predicting protein structures, identifying gene regulatory networks, and assisting in drug discovery through virtual screening, accelerating advancements in genomics, proteomics, and metabolomics research. This chapter aims to discuss the basic concept of generative models and their current and future scope in virology.
Indexed as
Identifiers
40455159What OpenQuestion 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 OpenQuestion graph.