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- W3087901980 abstract "Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research." @default.
- W3087901980 created "2020-10-01" @default.
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- W3087901980 date "2020-10-22" @default.
- W3087901980 modified "2023-10-12" @default.
- W3087901980 title "Prediction of driver variants in the cancer genome via machine learning methodologies" @default.
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- W3087901980 doi "https://doi.org/10.1093/bib/bbaa250" @default.
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