Biomarker discovery for risk stratification of cardiovascular events using an improved genetic algorithm

Xiaobo Zhou, Honghui Wang, Jun Wang, Gerard Hoehn, Joseph Azok, Marie Luise Brennan, Stanley L. Hazen, King Li, Stephen T.C. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detection of an optimal panel of biomarkers capable of predicting a patient's risk of major adverse cardiac events (MACE) is of clinical significance. Due to the high dynamic range of the protein concentration in human blood, applying proteomics techniques for protein profiling can generate large arrays of data for development of optimized clinical biomarker panels. The objective of this study is to discover a panel of biomarkers for predicting risk of MACE in subjects reliably. The development of immunoassay can only tolerate the complexity of the prediction model with less than ten selected biomarkers. Hence, traditional optimization methods, such as genetic algorithm, cannot be used to derive a solution in such a high-dimensional space. In this paper, we propose an improved genetic algorithm with the local floating searching technique to discover a subset of biomarkers with improved prognostic values for prediction of MACE. The proposed method has been compared with standard genetic algorithm and other feature selection approaches based on the MACE prediction experiments.

Original languageEnglish (US)
Title of host publication2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
DOIs
StatePublished - Dec 1 2006
Externally publishedYes
Event2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 - Bethesda, MD, United States
Duration: Jul 13 2006Jul 14 2006

Publication series

Name2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006

Other

Other2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
CountryUnited States
CityBethesda, MD
Period7/13/067/14/06

ASJC Scopus subject areas

  • Health(social science)
  • Assessment and Diagnosis
  • Medicine(all)
  • Health Information Management
  • Electrical and Electronic Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Signal Processing

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