Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes.

ABSTRACT:

Background

Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits.

Results

In this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm δ-TRIMAX. Its aim is to make opt

SUBMITTER: Bhar A 

PROVIDER: S-EPMC4480927 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature