ReviewMolecular biology of the cell2024
AI: A transformative opportunity in cell biology.
Review in Molecular biology of the cell, 2024. 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.
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- The Biological Function of Genome Organization.International journal of molecular sciences · 2025Review
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The success of artificial intelligence (AI) algorithms in predicting protein structure and more recently, protein interactions, demonstrates the power and potential of machine learning and AI for advancing and accelerating biomedical research. As cells are the fundamental unit of life, applying these tools to understand and predict cellular function represents the next great challenge. However, given the complexity of cellular structure and function, the diversity of cell types and the dynamic plasticity of cell states, the task will not be easy. To accomplish this challenge, AI models must scale and grow in sophistication, fueled by quantitative, multimodal data linking cell structure (their molecular composition, architecture, and morphology) to cell function (cell type and state). As cell biologists embrace the potential of AI models focused on cell features and functions, they are well positioned to contribute to their development, validate their utility, and perhaps, most importantly, play a leading role in leveraging the powers and insight emerging from the coming wave of cell-scale AI models.
Indexed as
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.