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S-Score: A Scoring System for the Identification and Prioritization of Predicted Cancer Genes

Overview of attention for article published in PLOS ONE, April 2014
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Title
S-Score: A Scoring System for the Identification and Prioritization of Predicted Cancer Genes
Published in
PLOS ONE, April 2014
DOI 10.1371/journal.pone.0094147
Pubmed ID
Authors

Jorge E. S. de Souza, André F. Fonseca, Renan Valieris, Dirce M. Carraro, Jean Y. J. Wang, Richard D. Kolodner, Sandro J. de Souza

Abstract

A new method, which allows for the identification and prioritization of predicted cancer genes for future analysis, is presented. This method generates a gene-specific score called the "S-Score" by incorporating data from different types of analysis including mutation screening, methylation status, copy-number variation and expression profiling. The method was applied to the data from The Cancer Genome Atlas and allowed the identification of known and potentially new oncogenes and tumor suppressors associated with different clinical features including shortest term of survival in ovarian cancer patients and hormonal subtypes in breast cancer patients. Furthermore, for the first time a genome-wide search for genes that behave as oncogenes and tumor suppressors in different tumor types was performed. We envisage that the S-score can be used as a standard method for the identification and prioritization of cancer genes for follow-up studies.

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Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 5%
United States 1 3%
Unknown 37 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 9 23%
Student > Master 5 13%
Professor > Associate Professor 4 10%
Professor 3 8%
Other 7 18%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 40%
Biochemistry, Genetics and Molecular Biology 11 28%
Computer Science 5 13%
Medicine and Dentistry 2 5%
Mathematics 1 3%
Other 0 0%
Unknown 5 13%