Dynamic Bayesian clustering.

J Bioinform Comput Biol

Department of Mathematics, Imperial College London, 180 Queens Gate, London SW7 2AZ, UK.

Published: October 2013

Clusters of time series data may change location and memberships over time; in gene expression data, this occurs as groups of genes or samples respond differently to stimuli or experimental conditions at different times. In order to uncover this underlying temporal structure, we consider dynamic clusters with time-dependent parameters which split and merge over time, enabling cluster memberships to change. These interesting time-dependent structures are useful in understanding the development of organisms or complex organs, and could not be identified using traditional clustering methods. In cell cycle data, these time-dependent structure may provide links between genes and stages of the cell cycle, whilst in developmental data sets they may highlight key developmental transitions.

Download full-text PDF

Source
http://dx.doi.org/10.1142/S0219720013420018DOI Listing

Publication Analysis

Top Keywords

cell cycle
8
dynamic bayesian
4
bayesian clustering
4
clustering clusters
4
clusters time
4
time series
4
data
4
series data
4
data change
4
change location
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!