Title |
Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations
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Published in |
BMC Bioinformatics, October 2011
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DOI | 10.1186/1471-2105-12-386 |
Pubmed ID | |
Authors |
Koen Van der Borght, Elke Van Craenenbroeck, Pierre Lecocq, Margriet Van Houtte, Barbara Van Kerckhove, Lee Bacheler, Geert Verbeke, Herman van Vlijmen |
Abstract |
Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Brazil | 1 | 4% |
Unknown | 24 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 6 | 24% |
Researcher | 4 | 16% |
Student > Doctoral Student | 2 | 8% |
Other | 2 | 8% |
Professor > Associate Professor | 2 | 8% |
Other | 5 | 20% |
Unknown | 4 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 6 | 24% |
Chemistry | 3 | 12% |
Agricultural and Biological Sciences | 3 | 12% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 8% |
Nursing and Health Professions | 1 | 4% |
Other | 6 | 24% |
Unknown | 4 | 16% |