This paper compares the performance of classification and regression trees (CART), multivariate adaptive regression splines (MARS), and a Gaussian mixture regressor (GMR) method in predicting breast cancer recurrence time in patients that have undergone cancer excision. It is shown that the GMR-based

induction and simplification algorithms to generate a simple, comprehensible set of rules for use in future model-based classification. The methods are de-scribed and their use is illustrated on real-world data.

the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a

so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers

objectives of breast cancer researches. Various omic-based techniques have been applied with increasing success to the molecular characterisation of breast tumours, which have resulted in a more detailed classification scheme and have produced clinical diagnostic tests that have been applied to both

of predictive markers like oestrogen receptor and human epidermal growth factor receptor 2 (HER2) to a more sophisticated classification comprising luminal A, luminal B, basal-like, HER2-positive and normal subgroups. In the laboratory, breast cancer is often modelled using established cell lines

of the tumor early in its course of development, the biology underlying the clinical course of the disease, and the selection and suc-cessful completion of the most appro-priate treatment for an individual pa-tient. The decision to stratify newly diagnosed breast cancer patients re-garding the need

Clinical management of breast cancer relies on case stratification, which increasingly employs molecular markers. The motivation behind delineating breast epithelial differentiation is to better target cancer cases through innate sensitivities bequeathed to the cancer from its normal progenitor

in the population-based Australian Breast Cancer Family Study (ABCFS). Results Median follow-up time was 4.9 years (range, 0.8 to 10.8 years). Compared with nulliparous women, women who gave birth within 2 years prior to diagnosis were more likely to have axillary node–positive (58 % v 41%; P .01), and estrogen

of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers