Jump to : Download | Abstract | Contact | BibTex reference | EndNote reference |


Xiaobo Zhou, Honghui Wang, Jun Wang, Yue Wang, Gerard Hoehn, Joseph Azok, Marie-Luise Brennan, Stanley L. Hazen, King and Chang, Shih-Fu Li, Stephen T. C. Wong. Identification of biomarkers for risk stratification of cardiovascular events using genetic algorithm with recursive local floating search. Proteomics, 9(8), 2009.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


Conventional biomarker discovery focuses mostly on the identification of single markers and thus oftenhas limited success indisease diagnosis and prognosis.This study proposes amethod toidentify an optimized protein biomarker panel based on MS studies for predicting the risk of major adverse cardiac events (MACE) in patients. Since the simplicity and concision requirement for the development of immunoassays can only tolerate the complexity of the prediction model with a very few selected discriminative biomarkers, established optimizationmethods, such as conventional genetic algorithm(GA), thus fails in the high-dimensional space. In this paper, we present a novel variant of GA that embeds the recursive local floating enhancement technique to discover a panel of protein biomarkerswith far better prognostic value for prediction ofMACE than existingmethods, including the one approved recently by FDA (Food and Drug Administration). The new pragmatic method applies the constraints ofMACE relevance and biomarker redundancy to shrink the local searching space in order to avoid heavy computation penalty resulted fromthe local floating optimization. The proposedmethod is comparedwith standardGAand other variable selection approaches based onthe MACE prediction experiments. Two powerful classification techniques, partial least squares logistic regression (PLS-LR) and support vector machine classifier (SVMC), are deployed as theMACE predictors owing to their ability in dealingwith small scale and binary response data.Newpreprocessing algorithms, such as low-level signal processing, duplicated spectra elimination, and outliner patient¡¯s samples removal, are also included in the proposedmethod. The experimental results show that an optimized panel of seven selected biomarkers can providemore than 77.1% MACE prediction accuracy using SVMC. The experimental resultsempirically demonstrate that the newGAalgorithmwith local floating enhancement (GA-LFE) can achieve the betterMACE prediction performance comparing with the existing techniques. The method has been applied to SELDI/MALDI MS datasets to discover an optimized panel of protein biomarkers to distinguish disease from control


Jun Wang
Yan Wang
Yong Wang

BibTex Reference

   Author = {Zhou, Xiaobo and Wang, Honghui and Wang, Jun and Wang, Yue and Hoehn, Gerard and Azok, Joseph and Brennan, Marie-Luise and Hazen, Stanley L. and Li, King       and Chang, Shih-Fu and Wong, Stephen T. C.},
   Title = {{Identification of biomarkers for risk stratification of cardiovascular events using genetic algorithm with recursive local floating search}},
   Journal = {Proteomics},
   Volume = {9},
   Number = {8},
   Publisher = {WILEY-VCH Verlag Weinheim},
   Year = {2009}

EndNote Reference [help]

Get EndNote Reference (.ref)


For problems or questions regarding this web site contact The Web Master.

This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).