ReviewBriefings in bioinformatics2026
Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.
Review in Briefings in bioinformatics, 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
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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
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Authors and funding
1 author.
Funding
Abstract
Mitotic checkpoints safeguard genomic integrity by orchestrating the precise segregation of chromosomes during cell division. Yet their complex, nonlinear dynamics have long defied full understanding through traditional experimental and computational approaches. In recent years, artificial intelligence (AI) has begun to transform this landscape. Machine learning and deep learning methods now achieve substantial accuracies in predicting cellular behaviors and uncovering novel regulatory mechanisms within checkpoint networks. Advances include transformer architectures capable of predicting spindle assembly checkpoint engagement with >95% accuracy, graph neural networks that decode kinetochore-microtubule dynamics at subpixel resolution, and hybrid AI-mechanistic models that reveal previously hidden feedback circuits. By integrating multi-omics data and bridging molecular mechanisms with clinical applications, AI-driven approaches are opening significant opportunities for precision medicine in cancer and other proliferative diseases. This review synthesizes emerging computational frameworks, highlights transformative AI-driven discoveries, and proposes a roadmap for developing predictive, personalized models of mitotic checkpoint control-charting a path from computational insight to clinical impact.
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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.