MT-SDREM statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.


Citations per year

Citations chart

Popular tool citations

chevron_left Signaling pathways Gene regulatory network inference chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases

MT-SDREM specifications


Unique identifier OMICS_23674
Alternative name Multi-Task Signaling and Dynamic Regulatory Events Miner
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Source code URL
Maintained Yes


Add your version


  • person_outline Ziv Bar-Joseph <>

Publication for Multi-Task Signaling and Dynamic Regulatory Events Miner

MT-SDREM in publication

PMCID: 5635550
PMID: 29017547
DOI: 10.1186/s12918-017-0471-8

[…] learning algorithms [] and have been applied to a number of different computational biology problems, most notably protein classification [] and gwas analysis [, ]. more recently, we have introduced mt-sdrem [], the first multi-task method for learning dynamic regulatory networks for multiple immune responses. mt-sdrem combines a graph orientation method with hidden markov models (hmms) […]

To access a full list of publications, you will need to upgrade to our premium service.

MT-SDREM institution(s)
Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA; Microsoft Research, Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Lane Center for Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
MT-SDREM funding source(s)
Supported by grants from the National Institute of Health (U01HL108642 and U01HL122626-01), the McDonnell Foundation program in Studying Complex Systems, the National Science Foundation DBI-1356505 and by the Microsoft Research.

MT-SDREM reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review MT-SDREM