The study by West et al. (2001) explores the use of gene expression profiles to predict clinical outcomes in human breast cancer. The researchers developed Bayesian regression models based on gene expression data from DNA microarray analysis of primary breast cancer samples. These models can discriminate breast tumors based on estrogen receptor (ER) status and lymph node status. The models were validated through cross-validation, assessing their utility and validity in predicting tumor status. The approach not only provides probabilistic predictions but also quantifies the uncertainties associated with these classifications. The study highlights the importance of gene expression analysis in improving diagnostic and therapeutic strategies for breast cancer, particularly in identifying subtypes that may respond differently to standard therapies. The results demonstrate that gene expression patterns can accurately classify tumors and predict their clinical status, offering a valuable tool for clinical decision-making.The study by West et al. (2001) explores the use of gene expression profiles to predict clinical outcomes in human breast cancer. The researchers developed Bayesian regression models based on gene expression data from DNA microarray analysis of primary breast cancer samples. These models can discriminate breast tumors based on estrogen receptor (ER) status and lymph node status. The models were validated through cross-validation, assessing their utility and validity in predicting tumor status. The approach not only provides probabilistic predictions but also quantifies the uncertainties associated with these classifications. The study highlights the importance of gene expression analysis in improving diagnostic and therapeutic strategies for breast cancer, particularly in identifying subtypes that may respond differently to standard therapies. The results demonstrate that gene expression patterns can accurately classify tumors and predict their clinical status, offering a valuable tool for clinical decision-making.