Analyzing Gene Expression Time-Courses

Simultaneously Segmenting Multiple Gene Expression Time Courses by Analyzing Cluster Dynamics

We present a new approach to segmenting multiple time series by analyzing the dynamics of cluster formation and rearrangement around putative segment boundaries. This approach finds application in distilling large numbers of gene expression profiles into temporal relationships underlying biological processes. By directly minimizing information-theoretic measures of segmentation quality derived ...

Analyzing time series gene expression data

MOTIVATION Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causa...

Independent arrays or independent time courses for gene expression time series data analysis

In this paper we apply three different independent component analysis (ICA) methods, including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to gene expression time series data and compare their performance in clustering genes and in finding biologically meaningful modes. Up to now, only spatial ICA was applied to gene expression data analysis. However, in the case of...

Analyzing tumor gene expression profiles

A brief introduction to high throughput technologies for measuring and analyzing gene expression is given. Various supervised and unsupervised data mining methods for analyzing the produced high-dimensional data are discussed. The main emphasis is on supervised machine learning methods for classification and prediction of tumor gene expression profiles. Furthermore, methods to rank the genes ac...

Analyzing gene expression in depression.