ArticleCancer research2009
Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.
Article in Cancer research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 272 papers.
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Who cites it
272 citing papers in PubMed, 480 citations in OpenAlex.
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- Consequences of human genetic variations inMolecular therapy. Advances · 2026Article
- Convergent Evolution in Tumor Genomes Targets Functional Domains.bioRxiv : the preprint server for biology · 2026Article
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- An interpretable molecular framework for predicting cancer driver missense mutations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- PathogenicThe journal of physical chemistry. B · 2025Article
- DeepGraphMut: a graph-based deep learning method for cancer prognosis using somatic mutation profile.Briefings in bioinformatics · 2025Article
- Identifying somatic driver mutations in cancer with a language model of the human genome.Computational and structural biotechnology journal · 2025Article
- Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes.Cell reports · 2024Article
- Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers.Scientific reports · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing.BMC medical genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- The receiver operating characteristic curve accurately assesses imbalanced datasets.Patterns (New York, N.Y.) · 2024Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Follicular lymphoma B cells exhibit heterogeneous transcriptional states with associated somatic alterations and tumor microenvironments.Cell reports. Medicine · 2024Article
- CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.PeerJ · 2024Article
- Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network.BMC bioinformatics · 2023Article
212 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Large-scale sequencing of cancer genomes has uncovered thousands of DNA alterations, but the functional relevance of the majority of these mutations to tumorigenesis is unknown. We have developed a computational method, called Cancer-specific High-throughput Annotation of Somatic Mutations (CHASM), to identify and prioritize those missense mutations most likely to generate functional changes that enhance tumor cell proliferation. The method has high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations (area under receiver operating characteristic curve, >0.91; area under Precision-Recall curve, >0.79). CHASM substantially outperformed previously described missense mutation function prediction methods at discriminating known oncogenic mutations in P53 and the tyrosine kinase epidermal growth factor receptor. We applied the method to 607 missense mutations found in a recent glioblastoma multiforme sequencing study. Based on a model that assumed the glioblastoma multiforme mutations are a mixture of drivers and passengers, we estimate that 8% of these mutations are drivers, causally contributing to tumorigenesis.
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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.