Composition-activity relationship modeling to predict the antitumor activity for quality control of curcuminoids from Curcuma longa L. (turmeric)

Analytical Methods

Volumn 5, Issue 3, 2013, Pages 641-647

  Jiang, Jian Lan ^a   Zhang, Huan ^a   Zhou, Pei Pei ^a   Han, Sheng Nan ^a   Han, Ya Di ^a   Yuan, Ying Jin ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MEAN SQUARE ERROR; PARTICLE SWARM OPTIMIZATION (PSO); QUALITY CONTROL;

ANTI-TUMOR ACTIVITIES; BEST MODEL; CHEMICAL CONSTITUENTS; CORRELATION COEFFICIENT; CURCUMA LONGA; CURCUMINOIDS; GA (GENETIC ALGORITHM); GRID-SEARCH ALGORITHM; HELA CELL; HIGH DEGREE OF ACCURACY; KERNEL FUNCTION; LINEAR KERNEL FUNCTIONS; OPTIMAL PARAMETER; POLYNOMIAL KERNELS; PSO(PARTICLE SWARM OPTIMIZATION); RADIAL BASIS; SUPPORT VECTOR REGRESSION (SVR); TRADITIONAL CHINESE MEDICINE;

GENETIC ALGORITHMS;

EID: 84872582644     PISSN: 17599660     EISSN: 17599679     Source Type: Journal    
DOI: 10.1039/c2ay26192h     Document Type: Article

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