Genetic fuzzy system predicting contractile reactivity patterns of small arteries

J Tang; Majid Sheykhzade; B F Clausen; H C M Boonen

doi:10.1038/psp.2014.3

Genetic fuzzy system predicting contractile reactivity patterns of small arteries

J Tang, Majid Sheykhzade, B F Clausen, H C M Boonen

1 Citationer (Scopus)

1053 Downloads (Pure)

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.

Originalsprog	Engelsk
Tidsskrift	C P T: Pharmacometrics & Systems Pharmacology
Vol/bind	3
Udgave nummer	4
Sider (fra-til)	1-9
Antal sider	9
ISSN	2163-8306
DOI	https://doi.org/10.1038/psp.2014.3
Status	Udgivet - 2 apr. 2014

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10.1038/psp.2014.3

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Citationsformater

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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}",

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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

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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.

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