Improving clustering with metabolic pathway data.

ABSTRACT:

Background

It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters.

Results

A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data

SUBMITTER: Milone DH 

PROVIDER: S-EPMC4002909 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature