ArticleGenome medicine2012
Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.
Article in Genome medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
What it found
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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
58 citing papers in PubMed, 107 citations in OpenAlex.
- Article
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- 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
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- 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
- Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.International journal of molecular sciences · 2023Review
- DriverMP enables improved identification of cancer driver genes.GigaScience · 2022Article
- iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.NAR genomics and bioinformatics · 2022Article
- Predicting functional consequences of mutations using molecular interaction network features.Human genetics · 2022Article
- Computational Resources for the Interpretation of Variations in Cancer.Advances in experimental medicine and biology · 2022Article
- Molecular-based precision oncology clinical decision making augmented by artificial intelligence.Emerging topics in life sciences · 2021Review
- Prediction of driver variants in the cancer genome via machine learning methodologies.Briefings in bioinformatics · 2021Review
- The Genomic Landscape of Actinic Keratosis.The Journal of investigative dermatology · 2021Article
- Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes.Cancers · 2021Article
- Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.BMC biology · 2021Article
- Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era.FEBS letters · 2020Review
- Possible A2E Mutagenic Effects on RPE Mitochondrial DNA from Innovative RNA-Seq Bioinformatics Pipeline.Antioxidants (Basel, Switzerland) · 2020Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational methods have been employed for that purpose, although most were originally developed to distinguish disease-related nonsynonymous single nucleotide variants (nsSNVs) from polymorphisms. Our new method, transformed Functional Impact score for Cancer (transFIC), improves the assessment of the functional impact of tumor nsSNVs by taking into account the baseline tolerance of genes to functional variants.
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.