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. Wong

doi:10.1109/LSSA.2006.250393

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. Wong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

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 language	English (US)
Title of host publication	2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
DOIs	https://doi.org/10.1109/LSSA.2006.250393
State	Published - Dec 1 2006
Event	2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 - Bethesda, MD, United States Duration: Jul 13 2006 → Jul 14 2006

Other

Other	2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
Country/Territory	United States
City	Bethesda, MD
Period	7/13/06 → 7/14/06

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

Access to Document

10.1109/LSSA.2006.250393

Cite this

Zhou, X, Wang, H, Wang, J, Hoehn, G, Azok, J, Brennan, ML, Hazen, SL, Li, K & Wong, ST 2006, Biomarker discovery for risk stratification of cardiovascular events using an improved genetic algorithm. in 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006., 4015794, 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006, Bethesda, MD, United States, 7/13/06. https://doi.org/10.1109/LSSA.2006.250393

@inproceedings{21901a9c300e4c68aa99a36308dad173,

title = "Biomarker discovery for risk stratification of cardiovascular events using an improved genetic algorithm",

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.",

author = "Xiaobo Zhou and Honghui Wang and Jun Wang and Gerard Hoehn and Joseph Azok and Brennan, {Marie Luise} and Hazen, {Stanley L.} and King Li and Wong, {Stephen T.}",

year = "2006",

month = dec,

day = "1",

doi = "10.1109/LSSA.2006.250393",

language = "English (US)",

isbn = "1424402786",

booktitle = "2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006",

note = "2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 ; Conference date: 13-07-2006 Through 14-07-2006",

}

TY - GEN

T1 - Biomarker discovery for risk stratification of cardiovascular events using an improved genetic algorithm

AU - Zhou, Xiaobo

AU - Wang, Honghui

AU - Wang, Jun

AU - Hoehn, Gerard

AU - Azok, Joseph

AU - Brennan, Marie Luise

AU - Hazen, Stanley L.

AU - Li, King

AU - Wong, Stephen T.

PY - 2006/12/1

Y1 - 2006/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=42749108274&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=42749108274&partnerID=8YFLogxK

U2 - 10.1109/LSSA.2006.250393

DO - 10.1109/LSSA.2006.250393

M3 - Conference contribution

AN - SCOPUS:42749108274

SN - 1424402786

SN - 9781424402786

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

T2 - 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006

Y2 - 13 July 2006 through 14 July 2006

ER -

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

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this