ReviewBiochemical Society transactions2026
Leveraging AI for cell biology discovery.
Review in Biochemical Society transactions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has become a transformative tool in cell biology, driving discoveries through the analysis of complex biological data. This review explores the diverse applications of AI, including its impact on microscopy, imaging, drug discovery, and synthetic biology. AI methods have significantly advanced our ability to analyze cellular images at single-cell resolution, uncover complex patterns in biological data, and predict cellular responses to various stimuli. Deep learning approaches have improved cell segmentation and tracking, facilitated precise single-cell transcriptomics analysis, and enhanced our understanding of protein structures and interactions. The application of AI to high-throughput technologies has also enabled detailed modeling of cell behavior. Key challenges are addressed, such as data quality requirements, model interpretability, and the need to democratize AI tools for broader accessibility in biology. Finally, the review considers future directions, highlighting AI's potential to advance basic research and therapeutic applications.
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.