Projects per year
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 towards 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.
Original language | English |
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Article number | bbaa250 |
Number of pages | 10 |
Journal | Briefings in Bioinformatics |
Volume | (2020) |
DOIs | |
Publication status | Published - 22 Oct 2020 |
Bibliographical note
Provisional acceptance date added, based on record creationKeywords
- varient prediction
- cancer
- machine learning
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Dive into the research topics of 'Prediction of driver variants in the cancer genome via machine learning methodologies'. Together they form a unique fingerprint.Projects
- 1 Finished
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Novel Methodology for Predicting the Functional Effects of Genetic Variation
Campbell, I. C. G. (Principal Investigator)
1/06/15 → 31/05/18
Project: Research