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 usi

SUBMITTER: Tang J 

PROVIDER: S-EPMC4011165 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature