ReviewComputational and structural biotechnology journal2020
Computational methods for detecting cancer hotspots.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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Who cites it
8 citing papers in PubMed.
- The Age of Molecular Biomarkers: Cancer in the Era of Personalized Medicine. What Do Pathologists in Developing Countries Need to Know and Understand?International journal of general medicine · 2026Review
- Identification of severity related mutation hotspots in SARS-CoV-2 using a density-based clustering approach.BioData mining · 2025Article
- The Impact of Mutational Hotspots on Cancer Survival.Cancers · 2024Article
- HotSPOT: A Computational Tool to Design Targeted Sequencing Panels to Assess Early Photocarcinogenesis.Cancers · 2023Article
- Development of a Sensitive Digital Droplet PCR Screening Assay for the Detection ofBiomedicines · 2023Article
- Multivariate analysis in data science for the geospatial distribution of the breast cancer mortality rate in Colombia.Frontiers in oncology · 2022Article
- Elucidation of the conformational dynamics and assembly of Argonaute-RNA complexes by distinct yet coordinated actions of the supplementary microRNA.Computational and structural biotechnology journal · 2022Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in
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