This study presents a method for predicting the clinical status of human breast cancer using gene expression profiles. The researchers developed Bayesian regression models based on gene expression data from DNA microarray analysis of primary breast cancer samples. These models can distinguish breast tumors based on estrogen receptor (ER) status and lymph node status. The models were validated using cross-validation techniques to assess their predictive accuracy and reliability. The study analyzed gene expression data from 13 ER+ LN+ tumors, 12 ER− LN+ tumors, 12 ER+ LN− tumors, and 12 ER− LN− tumors. RNA was extracted from frozen tumor tissue, and gene expression was measured using Affymetrix GENECHIP arrays. The researchers used binary regression models combined with singular value decomposition (SVD) and stochastic regularization to analyze the data. The models were tested for their ability to predict ER status and lymph node status with high accuracy. The results showed that the models could accurately predict ER status for most tumors, with only a few cases showing uncertainty. The models also demonstrated the ability to predict lymph node status with high accuracy. The study highlights the potential of gene expression profiling to improve the classification of breast cancer tumors and to inform clinical decisions. The analysis of gene expression profiles provides a more detailed understanding of tumor biology than traditional histopathological methods. The study emphasizes the importance of validating predictive models with cross-validation techniques to assess their reliability and to account for uncertainties in predictions. The results demonstrate that gene expression profiling can provide valuable insights into tumor biology and can be used to improve diagnostic and therapeutic strategies for breast cancer. The study also highlights the importance of further research to refine predictive models and to better understand the biological mechanisms underlying breast cancer.This study presents a method for predicting the clinical status of human breast cancer using gene expression profiles. The researchers developed Bayesian regression models based on gene expression data from DNA microarray analysis of primary breast cancer samples. These models can distinguish breast tumors based on estrogen receptor (ER) status and lymph node status. The models were validated using cross-validation techniques to assess their predictive accuracy and reliability. The study analyzed gene expression data from 13 ER+ LN+ tumors, 12 ER− LN+ tumors, 12 ER+ LN− tumors, and 12 ER− LN− tumors. RNA was extracted from frozen tumor tissue, and gene expression was measured using Affymetrix GENECHIP arrays. The researchers used binary regression models combined with singular value decomposition (SVD) and stochastic regularization to analyze the data. The models were tested for their ability to predict ER status and lymph node status with high accuracy. The results showed that the models could accurately predict ER status for most tumors, with only a few cases showing uncertainty. The models also demonstrated the ability to predict lymph node status with high accuracy. The study highlights the potential of gene expression profiling to improve the classification of breast cancer tumors and to inform clinical decisions. The analysis of gene expression profiles provides a more detailed understanding of tumor biology than traditional histopathological methods. The study emphasizes the importance of validating predictive models with cross-validation techniques to assess their reliability and to account for uncertainties in predictions. The results demonstrate that gene expression profiling can provide valuable insights into tumor biology and can be used to improve diagnostic and therapeutic strategies for breast cancer. The study also highlights the importance of further research to refine predictive models and to better understand the biological mechanisms underlying breast cancer.