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.
%0 Journal Article
%1 citeulike:1155023
%A Yu, Js
%A Ongarello, S.
%A Fiedler, R.
%A Chen, Xw
%A Toffolo, G.
%A Cobelli, C.
%A Trajanoski, Z.
%D 2005
%J Bioinformatics
%K unread
%N 10
%P 2200--2209
%R doi:10.1093/bioinformatics/bti370
%T Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data
%U http://dx.doi.org/doi:10.1093/bioinformatics/bti370
%V 21
%X 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.
@article{citeulike:1155023,
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.},
added-at = {2007-03-12T11:26:06.000+0100},
author = {Yu, Js and Ongarello, S. and Fiedler, R. and Chen, Xw and Toffolo, G. and Cobelli, C. and Trajanoski, Z.},
biburl = {https://www.bibsonomy.org/bibtex/200c03ffc3cda4d03403fce1a2f813a4a/hohkhkh1},
citeulike-article-id = {1155023},
doi = {doi:10.1093/bioinformatics/bti370},
interhash = {1612ce9081faacfbbba2aedaecb474e5},
intrahash = {00c03ffc3cda4d03403fce1a2f813a4a},
journal = {Bioinformatics},
keywords = {unread},
number = 10,
pages = {2200--2209},
priority = {3},
timestamp = {2007-03-12T11:37:45.000+0100},
title = {Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data},
url = {http://dx.doi.org/doi:10.1093/bioinformatics/bti370 },
volume = 21,
year = 2005
}