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


BACKGROUND
The software is available for academic and non-commercial institutions.
Yu
J S
JS
School of Electronics Engineering and Computer Science, Peking University, China.
Ongarello
S
S
Fiedler
R
R
Chen
X W
XW
Toffolo
G
G
Cobelli
C
C
Trajanoski
Z
Z
D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D010051:Ovarian Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins