Breast cancer patient stratification using a molecular regularized consensus clustering method

A Novel Clustering Method for Patient Stratification

1! A Novel Clustering Method for Patient Stratification Hongfu Liu, Rui Zhao, Hongsheng Fang, Feixiong Cheng, Yun Fu & Yang-Yu Liu Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. Chu Kochen Honors College, Zh...

Entropy-based Consensus Clustering for Patient Stratification Supplementary Information

Consensus Molecular Subtypes of Colorectal Cancer and their Clinical Implications

The colorectal cancer (CRC) subtyping consortium has unified six independent molecular classification systems, based on gene expression data, into a single consensus system with four distinct groups, known as the consensus molecular subtypes (CMS); clinical implications are discussed in this review based on articles relevant to the CMS of CRC indexed in PubMed as well as the authors’ own ...

Consensus Clustering And Fuzzy Classification For Breast Cancer Prognosis

Extracting usable and useful knowledge from large and complex data sets is a difficult and challenging problem. In this paper, we show how two complementary techniques have been used to tackle this problem in the context of breast cancer. Diagnosis concerns the identification of cancer within a patient; in contrast, prognosis concerns the prediction of the ongoing course of the disease, includi...

Clustering Breast Cancer Data by Consensus of Different Validity Indices

Clustering algorithms will, in general, either partition a given data set into a pre-specified number of clusters or will produce a hierarchy of clusters. In this paper we analyse several different clustering techniques and apply them to a particular data set of breast cancer data. When we do not know a priori which is the best number of groups, we use a range of different validity indices to t...