Given a biological network and a set of case-control studies, KeyPathwayMiner efficiently extracts all maximal connected sub-networks. These sub-networks contain the
genes that are mainly dysregulated, e.g., differentially expressed, in most cases studied.
The exact quantities for “mainly” and “most” are modeled with two easy-to-interpret parameters (K, L) that allows the user to control the number of
outliers (not dysregulated genes/cases) in the solutions.
We developed two slightly varying models (INES and GLONE) that
fall into the class of NP-hard optimization problems. To tackle the combinatorial explosion of the search space, we designed
a set of exact and heuristic algorithms.
With the introduction of version 4.0, KeyPathwayMiner was extended to be able to directly combine
several different omics data types.
Version 4.0 can further added support for integrating existing knowledge by adding a search
bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list.
The latest version 5.0 added extensive support for evaluating the robustness of the results upon perturbation of the network.