Linked Life Data

19458764

Source:http://linkedlifedata.com/resource/pubmed/id/19458764

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-5-21
pubmed:abstractText
In our previous study [1], we have compared the performance of a number of widely used discrimination methods for classifying ovarian cancer using Matrix Assisted Laser Desorption Ionization (MALDI) mass spectrometry data on serum samples obtained from Reflectron mode. Our results demonstrate good performance with a random forest classifier. In this follow-up study, to improve the molecular classification power of the MALDI platform for ovarian cancer disease, we expanded the mass range of the MS data by adding data acquired in Linear mode and evaluated the resultant decrease in classification error. A general statistical framework is proposed to obtain unbiased classification error estimates and to analyze the effects of sample size and number of selected m/z features on classification errors. We also emphasize the importance of combining biological knowledge and statistical analysis to obtain both biologically and statistically sound results.Our study shows improvement in classification accuracy upon expanding the mass range of the analysis. In order to obtain the best classification accuracies possible, we found that a relatively large training sample size is needed to obviate the sample variations. For the ovarian MS dataset that is the focus of the current study, our results show that approximately 20-40 m/z features are needed to achieve the best classification accuracy from MALDI-MS analysis of sera. Supplementary information can be found at http://bioinformatics.med.yale.edu/proteomics/BioSupp2.html.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
1176-9351
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
123-32
pubmed:year
2006
pubmed:articleTitle
Ovarian cancer classification based on mass spectrometry analysis of sera.
pubmed:affiliation
Department of Epidemiology and Public Health, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
pubmed:publicationType
Journal Article