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How does KeyPathwayMiner work?

  • 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.