ArticleiScience2020
A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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.
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Who cites it
5 citing papers in PubMed.
- Anticoagulant effects, substance basis, and quality assessment approach of Aspongopus chinensis Dallas.PloS one · 2025Article
- Exploring the Role of Clustered Mutations in Carcinogenesis and Their Potential Clinical Implications in Cancer.International journal of molecular sciences · 2024Review
- A mutation-level covariate model for mutational signatures.PLoS computational biology · 2023Article
- RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer.Cell systems · 2021Article
- Mutational Signatures: From Methods to Mechanisms.Annual review of biomedical data science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The characterization of mutational processes in terms of their signatures of activity relies mostly on the assumption that mutations in a given cancer genome are independent of one another. Recently, it was discovered that certain segments of mutations, termed processive groups, occur on the same DNA strand and are generated by a single process or signature. Here we provide a first probabilistic model of mutational signatures that accounts for their observed stickiness and strand coordination. The model conditions on the observed strand for each mutation and allows the same signature to generate a run of mutations. It can both use known signatures or learn new ones. We show that this model provides a more accurate description of the properties of mutagenic processes than independent-mutation achieving substantially higher likelihood on held-out data. We apply this model to characterize the processivity of mutagenic processes across multiple types of cancer.
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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 OpenQuestion graph.