Genetic fuzzy system predicting contractile reactivity patterns of small arteries

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Genetic fuzzy system predicting contractile reactivity patterns of small arteries. / Tang, J; Sheykhzade, Majid; Clausen, B F; Boonen, H C M.

In: C P T: Pharmacometrics & Systems Pharmacology, Vol. 3, No. 4, 02.04.2014, p. 1-9.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Tang, J, Sheykhzade, M, Clausen, BF & Boonen, HCM 2014, 'Genetic fuzzy system predicting contractile reactivity patterns of small arteries', C P T: Pharmacometrics & Systems Pharmacology, vol. 3, no. 4, pp. 1-9. https://doi.org/10.1038/psp.2014.3

APA

Tang, J., Sheykhzade, M., Clausen, B. F., & Boonen, H. C. M. (2014). Genetic fuzzy system predicting contractile reactivity patterns of small arteries. C P T: Pharmacometrics & Systems Pharmacology, 3(4), 1-9. https://doi.org/10.1038/psp.2014.3

Vancouver

Tang J, Sheykhzade M, Clausen BF, Boonen HCM. Genetic fuzzy system predicting contractile reactivity patterns of small arteries. C P T: Pharmacometrics & Systems Pharmacology. 2014 Apr 2;3(4):1-9. https://doi.org/10.1038/psp.2014.3

Author

Tang, J ; Sheykhzade, Majid ; Clausen, B F ; Boonen, H C M. / Genetic fuzzy system predicting contractile reactivity patterns of small arteries. In: C P T: Pharmacometrics & Systems Pharmacology. 2014 ; Vol. 3, No. 4. pp. 1-9.

Bibtex

@article{98273cdd794a4b34a82994d4390dd902,
title = "Genetic fuzzy system predicting contractile reactivity patterns of small arteries",
abstract = "Monitoring of physiological surrogate end points in drug development generates dynamic time-domain data reflecting the state of the biological system. Conventional data analysis often reduces the information in these data by extracting specific data points, thereby discarding potentially useful information. We developed a genetic fuzzy system (GFS) algorithm that is capable of learning all information in time-domain physiological data. Data on isometric force development of isolated small arteries were used as a framework for developing and optimizing a GFS. GFS performance was improved by several strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used in clustering or classification tasks as aids in the objective identification or prediction of dynamic physiological behavior.",
author = "J Tang and Majid Sheykhzade and Clausen, {B F} and Boonen, {H C M}",
year = "2014",
month = "4",
day = "2",
doi = "10.1038/psp.2014.3",
language = "English",
volume = "3",
pages = "1--9",
journal = "C P T: Pharmacometrics & Systems Pharmacology",
issn = "2163-8306",
publisher = "JohnWiley & Sons, Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Genetic fuzzy system predicting contractile reactivity patterns of small arteries

AU - Tang, J

AU - Sheykhzade, Majid

AU - Clausen, B F

AU - Boonen, H C M

PY - 2014/4/2

Y1 - 2014/4/2

N2 - Monitoring of physiological surrogate end points in drug development generates dynamic time-domain data reflecting the state of the biological system. Conventional data analysis often reduces the information in these data by extracting specific data points, thereby discarding potentially useful information. We developed a genetic fuzzy system (GFS) algorithm that is capable of learning all information in time-domain physiological data. Data on isometric force development of isolated small arteries were used as a framework for developing and optimizing a GFS. GFS performance was improved by several strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used in clustering or classification tasks as aids in the objective identification or prediction of dynamic physiological behavior.

AB - Monitoring of physiological surrogate end points in drug development generates dynamic time-domain data reflecting the state of the biological system. Conventional data analysis often reduces the information in these data by extracting specific data points, thereby discarding potentially useful information. We developed a genetic fuzzy system (GFS) algorithm that is capable of learning all information in time-domain physiological data. Data on isometric force development of isolated small arteries were used as a framework for developing and optimizing a GFS. GFS performance was improved by several strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used in clustering or classification tasks as aids in the objective identification or prediction of dynamic physiological behavior.

U2 - 10.1038/psp.2014.3

DO - 10.1038/psp.2014.3

M3 - Journal article

C2 - 24695357

VL - 3

SP - 1

EP - 9

JO - C P T: Pharmacometrics & Systems Pharmacology

JF - C P T: Pharmacometrics & Systems Pharmacology

SN - 2163-8306

IS - 4

ER -

ID: 106212597