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Bioinformatics. 2005 May 15;21(10):2200-9. Epub 2005 Mar 22.

Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data.

Author information

1
School of Electronics Engineering and Computer Science, Peking University, China.

Abstract

MOTIVATION:

High-throughput and high-resolution mass spectrometry instruments are increasingly used for disease classification and therapeutic guidance. However, the analysis of immense amount of data poses considerable challenges. We have therefore developed a novel method for dimensionality reduction and tested on a published ovarian high-resolution SELDI-TOF dataset.

RESULTS:

We have developed a four-step strategy for data preprocessing based on: (1) binning, (2) Kolmogorov-Smirnov test, (3) restriction of coefficient of variation and (4) wavelet analysis. Subsequently, support vector machines were used for classification. The developed method achieves an average sensitivity of 97.38% (sd = 0.0125) and an average specificity of 93.30% (sd = 0.0174) in 1000 independent k-fold cross-validations, where k = 2, ..., 10.

AVAILABILITY:

The software is available for academic and non-commercial institutions.

PMID:
15784749
DOI:
10.1093/bioinformatics/bti370
[Indexed for MEDLINE]
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