ReviewCancer research2025
Illuminating the Noncoding Genome in Cancer Using Artificial Intelligence.
Review in Cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- Advances in colorectal cancer screening: technological innovations, guideline discrepancies, and individualized strategies.Frontiers in oncology · 2025Review
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
Abstract
Understanding the vast noncoding cancer genome requires cutting-edge, high-resolution, and accessible strategies. Artificial intelligence is revolutionizing cancer research, enabling advanced models to analyze genome regulation. This review examines illustrative examples of noncoding mutations in cancer, focusing on both key regulatory elements and risk-associated variants that remain poorly understood, and compares key artificial intelligence models developed over the last decade for identifying functional noncoding variants, predicting gene expression impacts, and uncovering cancer-associated mutations. The discussion of the goals, data requirements, features, and outcomes of the models offers practical insights to help cancer researchers integrate these technologies into their work, regardless of computational expertise. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
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.