Literature DB >> 15784749

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

J S Yu1, S Ongarello, R Fiedler, X W Chen, G Toffolo, C Cobelli, Z Trajanoski.   

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.

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Year:  2005        PMID: 15784749     DOI: 10.1093/bioinformatics/bti370

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  27 in total

1.  A new approach for the analysis of mass spectrometry data for biomarker discovery.

Authors:  N Barbarini; P Magni; R Bellazzi
Journal:  AMIA Annu Symp Proc       Date:  2006

2.  A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform.

Authors:  Hussain Montazery-Kordy; Mohammad Hossein Miran-Baygi; Mohammad Hassan Moradi
Journal:  J Zhejiang Univ Sci B       Date:  2008-11       Impact factor: 3.066

3.  Enhanced Evolutionary Feature Selection and Ensemble Method for Cardiovascular Disease Prediction.

Authors:  V Jothi Prakash; N K Karthikeyan
Journal:  Interdiscip Sci       Date:  2021-05-14       Impact factor: 2.233

4.  PHARM - Association Rule Mining for Predictive Health.

Authors:  Chih-Wen Cheng; Greg S Martin; Po-Yen Wu; May D Wang
Journal:  IFMBE Proc       Date:  2014

Review 5.  Data mining in healthcare and biomedicine: a survey of the literature.

Authors:  Illhoi Yoo; Patricia Alafaireet; Miroslav Marinov; Keila Pena-Hernandez; Rajitha Gopidi; Jia-Fu Chang; Lei Hua
Journal:  J Med Syst       Date:  2011-05-03       Impact factor: 4.460

6.  Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.

Authors:  Haydn Hoffman; Sunghoon I Lee; Jordan H Garst; Derek S Lu; Charles H Li; Daniel T Nagasawa; Nima Ghalehsari; Nima Jahanforouz; Mehrdad Razaghy; Marie Espinal; Amir Ghavamrezaii; Brian H Paak; Irene Wu; Majid Sarrafzadeh; Daniel C Lu
Journal:  J Clin Neurosci       Date:  2015-06-23       Impact factor: 1.961

7.  Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data.

Authors:  Kai-Lin Tang; Tong-Hua Li; Wen-Wei Xiong; Kai Chen
Journal:  BMC Bioinformatics       Date:  2010-02-27       Impact factor: 3.169

8.  Accurate peak list extraction from proteomic mass spectra for identification and profiling studies.

Authors:  Nicola Barbarini; Paolo Magni
Journal:  BMC Bioinformatics       Date:  2010-10-16       Impact factor: 3.169

9.  Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines.

Authors:  Wei Guan; Manshui Zhou; Christina Y Hampton; Benedict B Benigno; L Deette Walker; Alexander Gray; John F McDonald; Facundo M Fernández
Journal:  BMC Bioinformatics       Date:  2009-08-22       Impact factor: 3.169

10.  A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection.

Authors:  Michele Ceccarelli; Antonio d'Acierno; Angelo Facchiano
Journal:  BMC Bioinformatics       Date:  2009-10-15       Impact factor: 3.169

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