- Article: Our paper "Analyzing time course gene expression data with biological and technical replicates to estimate gene networks by state space models" has been accepted by 2nd Asia International Conference on Modelling & Simulation. (06-Mar-2008)
- Article: Our paper "Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models" has been accepted by Bioinformatics. (01-Jan-2008)
- Our paper "Clustering samples characterized by time course gene expression profiles using the mixture of state space models'' has been accepted as Proc. the Seventh Annual International Workshop on Bioinformatics and Systems Biology (IBSB) 2007.(28-Sep-2007)
- Invited poster presentation: "Module-based gene network construction with state-space models'' IEEE Statistical Signal Processing Workshop . (28-Sep-2007)
- Oral Presentation: 2007 IASC-ARS special conference (June 7-8, 2007, Seoul Korea) (15-Mar-2007)
- TRANS-MNET has been uploaded (14-Mar-2007)
- Our paper "Finding module-based gene networks" has been appered in IEEE Signal Processing Magagine, 24 .37-46. (09-Mar-2007)
Overview
TRANS-MNET available here implements the time series analysis of gene expression profiles
with the state space model. The current version (last modified; 07-Mar-2007) is executable
for Windows only and provided as an executable format. The source codes are written by C
language (Osamu Hirose).
The application software provides us an integrated analytic tool for time
course gene expression analysis. It contains a wide variety of applications
as follows:
- Automatic extraction of genes relevant to temporal process of gene regulatory program.
- Statistical identification of transcription modules of genes
- Inference of temporal dependency between the estimated transcription modules (module interactions)
- Statistical evaluation of temporal dependency between individual genes (gene-level interactions).
Figure 1: Time series analysis of gene expression profiles with TRANS-MNET and a snapshot of graphical images which are created by R scripts that we developed. The method explores the potential transcriptional modules of genes which are relevant to the temporal gene expression program behind time series gene expression profiles, and simultaneously, evaluates the degree of connectivities across the overall genes.
