Genetic fuzzy system predicting contractile reactivity patterns of small arteries

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt


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.
TidsskriftC P T: Pharmacometrics & Systems Pharmacology
Udgave nummer4
Sider (fra-til)1-9
Antal sider9
StatusUdgivet - 2 apr. 2014

Antal downloads er baseret på statistik fra Google Scholar og

Ingen data tilgængelig

ID: 106212597