An Improved FMM Neural Network for Classification of Gene Expression Data.
ABSTRACT Gene microarray experiment can monitor the expression of thousands of genes simultaneously. Using the promising technology,
accurate classification of tumor subtypes becomes possible, allowing for specific treatment that maximizes efficacy and minimizes
toxicity. Meanwhile, optimal genes selected from microarray data will contribute to diagnostic and prognostic of tumors in
low cost. In this paper, we propose an improved FMM (fuzzy Min-Max) neural network classifier which provides higher performance
than the original one. The improved one can automatically reduce redundant hyperboxes thus it can solve difficulty of setting
the parameter θ value and is able to select discriminating genes. Finally we apply our improved classifier on the small, round blue-cell
tumors dataset and get good results.
- IEEE Transactions on Neural Networks. 01/1992;
- [Show abstract] [Hide abstract]
ABSTRACT: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.New England Journal of Medicine 12/2002; 347(25):1999-2009. · 51.66 Impact Factor