ArticleGenome medicine2021
A mixture model for signature discovery from sparse mutation data.
Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Mutational signature refitting on sparse pan-cancer data.Algorithms for molecular biology : AMB · 2026Article
- Mutational signatures in hematological malignancies.Einstein (Sao Paulo, Brazil) · 2026Review
- C-ziptf: stable tensor factorization for zero-inflated multi-dimensional genomics data.BMC bioinformatics · 2024Article
- Cancer mutational signatures identification in clinical assays using neural embedding-based representations.Cell reports. Medicine · 2024Article
- A mutation-level covariate model for mutational signatures.PLoS computational biology · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- A Biterm Topic Model for Sparse Mutation Data.Cancers · 2023Article
- Article
- Spectrum of DNA mismatch repair failures viewed through the lens of cancer genomics and implications for therapy.Clinical science (London, England : 1979) · 2022Article
- ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- A mixture model for signature discovery from sparse mutation data.Genome medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM .
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