eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.

Source: Cancer Informatics - October 21, 2014 Category: Cancer & Oncology Authors: Zheng ChangZhenjia WangCody AshbyChuan ZhouGuojun LiShuzhong ZhangXiuzhen Huang Source Type: research