ReviewBriefings in bioinformatics2026
Computational prediction of replication origins: a comparative review of methods, benchmarks, and trends from heuristics to deep learning.
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.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
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Abstract
Origins of DNA replication (Ori) are genomic loci where DNA synthesis begins, essential for bacterial viability, eukaryotic development, and genome integrity. While early computational methods relied on compositional skews and motif heuristics, recent years have seen a shift toward artificial intelligence approaches. This review systematically surveys Ori prediction methods published through August 2025, integrating biological background with curated datasets, benchmarks, and a classification of computational strategies from rule-based models to deep learning. We summarize performance across organisms, cell types, and platforms, and highlight key trends, challenges, and opportunities in benchmarking, interpretability, and cross-species generalization-providing both an accessible entry point for practitioners and a roadmap for future methodological development.
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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.