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http://hdl.handle.net/1942/8049
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DC Field | Value | Language |
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dc.contributor.author | VALKENBORG, Dirk | - |
dc.contributor.author | VAN SANDEN, Suzy | - |
dc.contributor.author | LIN, Dan | - |
dc.contributor.author | KASIM, Adetayo | - |
dc.contributor.author | JANSEN, Ivy | - |
dc.contributor.author | SHKEDY, Ziv | - |
dc.contributor.author | BURZYKOWSKI, Tomasz | - |
dc.contributor.author | HALDERMANS, Philippe | - |
dc.contributor.author | ZHU, Qi | - |
dc.date.accessioned | 2008-03-20T09:55:52Z | - |
dc.date.available | NO_RESTRICTION | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 7(2) | - |
dc.identifier.issn | 1544-6115 | - |
dc.identifier.uri | http://hdl.handle.net/1942/8049 | - |
dc.description.abstract | We present an approach to construct a classification rule based on the mass spectrometry data provided by the organizers of the "Classification Competition on Clinical Mass Spectrometry Proteomic Diagnosis Data". Before constructing a classification rule, we attempted to pre-process the data and to select features of the spectra that were likely due to true biological signals (i.e. peptides/proteins). As a result, we selected a set of 92 features. To construct the classification rule, we considered eight methods for selecting a subset of the features, combined with seven classification methods. The performance of the resulting 56 combinations was evaluated by using a cross-validation procedure with 1000 re-sampled data sets. The best result, as indicated by the lowest overall misclassification rate, was obtained by using the whole set of 92 features as the input for a support-vector machine(SVM) with a linear kernel. This method was therefore used to construct the classification rule. For the training data set, the total error rate for the classification rule, as estimated by using leave-one-out-cross-validation, was equal to 0.16, with the sensitivity and specificity equal to 0.87 and 0.82 respectively. | - |
dc.language.iso | en | - |
dc.publisher | BERKELEY ELECTRONIC PRESS | - |
dc.title | A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry | - |
dc.type | Journal Contribution | - |
dc.identifier.issue | 2 | - |
dc.identifier.volume | 7 | - |
local.format.pages | 22 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.isi | 000254568100009 | - |
item.validation | ecoom 2009 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.fullcitation | VALKENBORG, Dirk; VAN SANDEN, Suzy; LIN, Dan; KASIM, Adetayo; JANSEN, Ivy; SHKEDY, Ziv; BURZYKOWSKI, Tomasz; HALDERMANS, Philippe & ZHU, Qi (2008) A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry. In: STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 7(2). | - |
item.contributor | VALKENBORG, Dirk | - |
item.contributor | VAN SANDEN, Suzy | - |
item.contributor | LIN, Dan | - |
item.contributor | KASIM, Adetayo | - |
item.contributor | JANSEN, Ivy | - |
item.contributor | SHKEDY, Ziv | - |
item.contributor | BURZYKOWSKI, Tomasz | - |
item.contributor | HALDERMANS, Philippe | - |
item.contributor | ZHU, Qi | - |
crisitem.journal.issn | 2194-6302 | - |
crisitem.journal.eissn | 1544-6115 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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A Cross-validation Study to Select.pdf | Peer-reviewed author version | 9.02 MB | Adobe PDF | View/Open |
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