Applying of hierarchical clustering to analysis of protein patterns in the human cancer-associated liver.

ABSTRACT:

Background

There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters) of samples and to explore underlying data structure (unsupervised learning).

Results

We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE). Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18) revealed a pair of clusters, containing 11 and 7 samples. Previous

SUBMITTER: Petushkova NA 

PROVIDER: S-EPMC4118999 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature