A filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. TMRMR incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. The incorporation of the temporal information into the feature selection process leads to selection of more discriminative features.

User report

0 user reviews

0 user reviews

No review has been posted.

TMRMR forum

No open topic.

TMRMR classification

TMRMR specifications

Software type:
Package
Restrictions to use:
None
Output data:
The ranked list of the top genes.
Computer skills:
Advanced
Interface:
Command line interface
Input data:
A set of temporally aligned gene expression data.
Programming languages:
MATLAB
Stability:
Stable
Source code URL:
https://github.com/radovicmiloskg/TMRMR.git

Publications

  • (Radovic et al., 2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics.
    PMID: 28049413

Credits

Institution(s)

Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, Philadelphia, PA, USA; Bioengineering Research and Development Center - BioIRC, Kragujevac, Serbia; Mathematics Department, Faculty of Science, Ain Shams University, Cairo, Egypt; Center for Computational Health, IBM T.J. Watson Research Center, Cambridge, MA, USA; Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

Funding source(s)

This work was partially supported by the Defense Advanced Research Projects Agency (DARPA) and the Army Research Office (ARO) under Contract No. W911NF-16-C-0050, by DARPA grant No. 66001-11-1-4183 negotiated by SSC Pacific grant, and Serbian Ministry of Education, Science and Technological Development grants III41007 and ON174028.

Related Time course tools

Most Recent Tools

Desktop app
Transcript Time… Transcript Time Course Analysis

TTCA Transcript Time Course Analysis

Analyses sparse and heterogeneous time course data with high detection…

Analyses sparse and heterogeneous time course data with high detection sensitivity and transparency. TTCA is specifically designed for the analysis of perturbation responses. It combines different…

SwitchFinder SwitchFinder

SwitchFinder

Allows for capturing important features of the gene dynamic behaviour.…

Allows for capturing important features of the gene dynamic behaviour. SwitchFinder is a statistical method for the analysis of time-series data, in particular gene expression data, based on a…

Desktop app
EXpression Analyzer… EXpression Analyzer and DisplayER

EXPANDER EXpression Analyzer and DisplayER

An integrated software platform for the analysis of microarray gene expression…

An integrated software platform for the analysis of microarray gene expression data. EXPANDER is designed to support all the stages of microarray data analysis, from raw data normalization to…

Desktop app
G T A T C G C T A ImpulseDE ImpulseDE

ImpulseDE

An R package suited to capture single impulse-like progression patterns in high…

An R package suited to capture single impulse-like progression patterns in high throughput time series datasets. ImpulseDE can be applied on any kind of high throughput gene expression data. It…

Most Popular Tools

Desktop app
Short Time-series… Short Time-series Expression Miner

STEM Short Time-series Expression Miner

A software program specifically designed for the analysis of short time series…

A software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also…

10 related tools

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.