Using Classification and K‑means Methods to Predict Breast Cancer Recurrence in Gene Expression Data

Background: Breast cancer is a type of cancer that starts in the breast tissue and affects about ۱۰% of women at different stages of their lives. In this study, we applied a new method to predict recurrence in biological networks made from gene expression data. Method: The method includes the steps such as data collection, clustering, determining differentiating genes, and classification. The eight techniques consist of random forest, support vector machine and neural network, randomforest + k‑means, hidden markov model, joint mutual information, neural network + k‑means and suportvector machine + k‑menas were implemented on ۱۲۱۷۲ genes and ۲۰۰ samples. Results: Thirty genes were considered as differentiating genes which used for the classification. The results showed that random forest + k‑means get better performance than other techniques. The two techniques including neural network + k‑means and random forest + k‑means performed better than other techniques in identifying high risk cases. Conclusion: Thirty of ۱۲,۱۷۲ genes are considered for classification that the use of clustering has improved the classification techniques performance.

Keywords:

Classification , gene , K‑means