Skip to content
MIPRIP
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Example_data
MIPRIP_v1
MIPRIP_v2 Update Nov 13, 2019
Regulatory_Networks
README.md

README.md

MIPRIP

MIPRIP: The Mixed Integer linear Programming based Regulatory Interaction Predictor

MIPRIP is a software package for R (www.r-project.org) to predict regulators of a gene of interest from gene expression profiles of the samples under study and known regulator binding information (from e.g. ChIP-seq/ChIP-chip databases). It was initially developed to study the regulation of the telomerase genes of Saccharomyces cerevisiae from knockout strains of short telomere length compared to controls (normal telomere length) (MIPRIP v1). The extended version of MIPRIP (MIPRIP 2.0 or v2) can be used either to predict regulators of one group of samples (single-mode), to identify significant regulators being different between two groups of samples (e.g. disease vs. control) (dual-mode), or can be applied to more than two groups (multi-mode) to identify the most common and group-specific regulators. It was developed to study the regulation of the telomerase in human, but MIPRIP can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, disease/healthy or treatment/controls. With our newly constructed generic human or mouse regulatory network, MIPRIP 2.0 is applicable to human, mouse and yeast gene expression data.

Best, you follow the tutorial in the short manuals which explains how to use the method along with our case studies.

For more details you can read our papers:

Poos, A.M., et al. Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach. bioRxiv 2019, 513259; doi: https://doi.org/10.1101/513259.

Poos, A.M., et al. Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast. Nucleic Acids Res 2016;44(10):e93.

You can’t perform that action at this time.