Optimizing molecular signatures for predicting prostate cancer recurrence

Prostate

Volumn 69, Issue 10, 2009, Pages 1119-1127

  Sun, Yijun ^a   Goodison, Steve ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b UNIVERSITY OF FLORIDA HEALTH SCIENCE CENTER   (United States)

Author keywords

Microarray; Nomogram; Predictive model; Prostate cancer prognosis

Indexed keywords

ALGORITHM; ARTICLE; CANCER RECURRENCE; CONTROLLED STUDY; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; GENETIC PREDISPOSITION; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; PROSTATE CANCER; SENSITIVITY AND SPECIFICITY; BIOLOGY; COMPARATIVE STUDY; DISEASE COURSE; DNA MICROARRAY; FEASIBILITY STUDY; GENE EXPRESSION REGULATION; GENETICS; METHODOLOGY; PREDICTION AND FORECASTING; PROGNOSIS; PROSTATE TUMOR; PROSTATECTOMY; TUMOR RECURRENCE;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DISEASE PROGRESSION; FEASIBILITY STUDIES; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; MALE; NEOPLASM RECURRENCE, LOCAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PREDICTIVE VALUE OF TESTS; PROGNOSIS; PROSTATECTOMY; PROSTATIC NEOPLASMS;

EID: 67449132037     PISSN: 02704137     EISSN: 10970045     Source Type: Journal    
DOI: 10.1002/pros.20961     Document Type: Article

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