Chemometrics-based approach to modeling quantitative composition-activity relationships for Radix Tinosporae

Interdisciplinary Sciences – Computational Life Sciences

Volumn 2, Issue 3, 2010, Pages 221-227

  Yan, Shi Kai ^a   Lin, Zhong Ying ^b   Dai, Wei Xing ^b   Shi, Qi Rong ^b   Liu, Xiao Hua ^b   Jin, Hui Zi ^a   Zhang, Wei Dong ^a,b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b SECOND MILITARY MEDICAL UNIVERSITY   (China)

Author keywords

genetic algorithm; quantitative composition activity relationship; Radix Tinosporae; support vector regression; traditional Chinese medicine

Indexed keywords

MUS;

ANALGESIC AGENT; HERBACEOUS AGENT;

ABDOMINAL PAIN; ALGORITHM; ANIMAL; ANIMAL BEHAVIOR; ARTICLE; BIOLOGICAL MODEL; CHEMISTRY; CHINESE MEDICINE; DRUG DEVELOPMENT; DRUG EFFECT; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; INSTITUTE FOR CANCER RESEARCH MOUSE; MALE; MOUSE; PLANT ROOT; QUALITY CONTROL; REGRESSION ANALYSIS; STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE; TINOSPORA;

ABDOMINAL PAIN; ALGORITHMS; ANALGESICS; ANIMALS; BEHAVIOR, ANIMAL; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; DRUG DISCOVERY; DRUGS, CHINESE HERBAL; LEAST-SQUARES ANALYSIS; MALE; MEDICINE, CHINESE TRADITIONAL; MICE; MICE, INBRED ICR; MODELS, BIOLOGICAL; PLANT ROOTS; QUALITY CONTROL; REGRESSION ANALYSIS; STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINES; TINOSPORA;

EID: 84872590067     PISSN: 19132751     EISSN: 18671462     Source Type: Journal    
DOI: 10.1007/s12539-010-0026-9     Document Type: Article

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