Title |
Pairwise protein expression classifier for candidate biomarker discovery for early detection of human disease prognosis
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Published in |
BMC Bioinformatics, August 2012
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DOI | 10.1186/1471-2105-13-191 |
Pubmed ID | |
Authors |
Parminder Kaur, Daniela Schlatzer, Kenneth Cooke, Mark R Chance |
Abstract |
An approach to molecular classification based on the comparative expression of protein pairs is presented. The method overcomes some of the present limitations in using peptide intensity data for class prediction for problems such as the detection of a disease, disease prognosis, or for predicting treatment response. Data analysis is particularly challenging in these situations due to sample size (typically tens) being much smaller than the large number of peptides (typically thousands). Methods based upon high dimensional statistical models, machine learning or other complex classifiers generate decisions which may be very accurate but can be complex and difficult to interpret in simple or biologically meaningful terms. A classification scheme, called ProtPair, is presented that generates simple decision rules leading to accurate classification which is based on measurement of very few proteins and requires only relative expression values, providing specific targeted hypotheses suitable for straightforward validation. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 50% |
India | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 5% |
Unknown | 37 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 11 | 28% |
Student > Ph. D. Student | 7 | 18% |
Professor | 3 | 8% |
Other | 3 | 8% |
Student > Doctoral Student | 2 | 5% |
Other | 5 | 13% |
Unknown | 8 | 21% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 9 | 23% |
Biochemistry, Genetics and Molecular Biology | 8 | 21% |
Computer Science | 6 | 15% |
Medicine and Dentistry | 3 | 8% |
Chemistry | 2 | 5% |
Other | 2 | 5% |
Unknown | 9 | 23% |