ReviewFundamental research2026
Application of artificial intelligence in life science: Historical review and future perspectives.
Review in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
- Multiparameter Simulation for the Optimization of Ion Transmission Efficiency Through Mass Spectrometry Inlet Capillary.Rapid communications in mass spectrometry : RCM · 2026Article
- Epigenetic regulation of metabolism in Saccharomyces cerevisiae: mechanisms, metabolic crosstalk, and engineering applications.Molecular biology reports · 2026Review
- AI for biology: Catalyzing interdisciplinary innovation to unravel life's complexity and address biomedical challenges.Fundamental research · 2026Article
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
The fast advancement of artificial intelligence (AI) technologies in recent years, especially the deep learning algorithm and transformer neural network, has brought great impacts on scientific researches. As a displine focusing on curation and analysis of life science data, bioinformatics has experienced remarkable changes under the impetus of AI technologies, not only in nowadays, but also throughout its history. In this review, we firstly summarize the historical events of computer-assisted life science data analysis, then assess the features, contributions and changes of AI methods in life science research by using bibliometric analysis, and discuss the future challenges for AI methods from the life science research aspects. There is no doubt that AI technologies will continuously accelerate and revolutionize life science research in the future, in the meanwhile, the development of new AI methods more suitable for life science data is also in great needs.
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Identifiers
What 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.