Title |
Knowledge Driven Variable Selection (KDVS) – a new approach to enrichment analysis of gene signatures obtained from high–throughput data
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Published in |
Source Code for Biology and Medicine, January 2013
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DOI | 10.1186/1751-0473-8-2 |
Pubmed ID | |
Authors |
Grzegorz Zycinski, Annalisa Barla, Margherita Squillario, Tiziana Sanavia, Barbara Di Camillo, Alessandro Verri |
Abstract |
High-throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score-based or requires tunable parameters as well, limiting its power. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Luxembourg | 1 | 50% |
Germany | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Italy | 2 | 5% |
United States | 1 | 2% |
Germany | 1 | 2% |
Luxembourg | 1 | 2% |
Unknown | 36 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 14 | 34% |
Student > Ph. D. Student | 11 | 27% |
Student > Master | 4 | 10% |
Professor > Associate Professor | 3 | 7% |
Student > Bachelor | 2 | 5% |
Other | 6 | 15% |
Unknown | 1 | 2% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 15 | 37% |
Agricultural and Biological Sciences | 13 | 32% |
Engineering | 4 | 10% |
Mathematics | 2 | 5% |
Medicine and Dentistry | 2 | 5% |
Other | 3 | 7% |
Unknown | 2 | 5% |