Title |
Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation
|
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Published in |
Genome Medicine, November 2012
|
DOI | 10.1186/gm390 |
Pubmed ID | |
Authors |
Abel Gonzalez-Perez, Jordi Deu-Pons, Nuria Lopez-Bigas |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 3 | 33% |
United Kingdom | 2 | 22% |
Australia | 1 | 11% |
Venezuela, Bolivarian Republic of | 1 | 11% |
Unknown | 2 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 6 | 67% |
Members of the public | 2 | 22% |
Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 4% |
Italy | 3 | 2% |
Spain | 3 | 2% |
Korea, Republic of | 2 | 1% |
Sweden | 1 | <1% |
Singapore | 1 | <1% |
Germany | 1 | <1% |
United Kingdom | 1 | <1% |
Argentina | 1 | <1% |
Other | 0 | 0% |
Unknown | 130 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 39 | 26% |
Student > Ph. D. Student | 37 | 25% |
Student > Master | 14 | 9% |
Student > Bachelor | 14 | 9% |
Other | 9 | 6% |
Other | 19 | 13% |
Unknown | 17 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 59 | 40% |
Biochemistry, Genetics and Molecular Biology | 26 | 17% |
Computer Science | 16 | 11% |
Medicine and Dentistry | 9 | 6% |
Engineering | 3 | 2% |
Other | 13 | 9% |
Unknown | 23 | 15% |