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Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN(More)
BACKGROUND Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0). METHODS Pooled 16 datasets contained 88 normal(More)
BACKGROUND Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. OBJECTIVE Microarray gene expression data from(More)
OBJECTIVE Bipolar disorder (BD) and attention-deficit/hyperactivity disorder (ADHD) have been associated with the use of cigarettes, but little is known about the impact of the subthreshold symptoms of BD or ADHD on the course of nicotine dependence. Identifying the links is essential for elucidating the pathway and supporting the development of nicotine(More)
The project demonstrated to analyze the survivability of cervical cancer from the large data set of SEER (Surveillance Epidemiology and End Results). The data were re-sampled into 10 folds on 5 different size that were based on for three methods- artificial neural network, logistic regression and decision tree- to establish models for predicting the(More)
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