A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib

Biotechnology and Bioengineering

Volumn 109, Issue 1, 2012, Pages 213-224

  Prasasya, Rexxi D ^a   Vang, Kang Z ^b   Kreeger, Pamela K ^a,b  

^a University of Wisconsin Madison   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Epithelial ovarian cancer; ErbB network; Partial least squares regression; Systems biology

Indexed keywords

AUTOCRINE LOOP; CELL LINES; CI-1033; CLINICAL PROBLEMS; COMPLEX DISEASE; FULL MODEL; MODEL ANALYSIS; MULTIVARIATE FUNCTION; MULTIVARIATE MODELS; NETWORK COMPOSITION; OPTIMAL TREATMENT; OVARIAN CANCER CELLS; OVARIAN CANCERS; PARTIAL LEAST SQUARES REGRESSION; RECEPTOR LEVELS; SYSTEMS BIOLOGY; TRAINING SETS;

CELL CULTURE; CELL DEATH; DRUG THERAPY; FORECASTING; LIGANDS; REGRESSION ANALYSIS;

DISEASES;

CANERTINIB; EPIDERMAL GROWTH FACTOR RECEPTOR;

ANTINEOPLASTIC ACTIVITY; ARTICLE; AUTOCRINE EFFECT; CYTOTOXICITY; FEMALE; HUMAN; HUMAN CELL; OVARY CANCER; TREATMENT RESPONSE;

ANTINEOPLASTIC AGENTS; FEMALE; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MORPHOLINES; OVARIAN NEOPLASMS; RECEPTOR, EPIDERMAL GROWTH FACTOR; SYSTEMS BIOLOGY;

EID: 81455158899     PISSN: 00063592     EISSN: 10970290     Source Type: Journal    
DOI: 10.1002/bit.23297     Document Type: Article

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