Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data
Bioinformatics, 21 (10):
2200--2209 (2005)DOI: doi:10.1093/bioinformatics/bti370
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.
