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license:
- GPL-2.0-only
- GPL-3.0-only
title: 'KBoost: Inference of gene regulatory networks from gene expression data'
version: 1.13.0
abstract: Reconstructing gene regulatory networks and transcription factor activity
  is crucial to understand biological processes and holds potential for developing
  personalized treatment. Yet, it is still an open problem as state-of-art algorithm
  are often not able to handle large amounts of data. Furthermore, many of the present
  methods predict numerous false positives and are unable to integrate other sources
  of information such as previously known interactions. Here we introduce KBoost,
  an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging
  for fast and accurate reconstruction of gene regulatory networks. KBoost can also
  use a prior network built on previously known transcription factor targets. We have
  benchmarked KBoost using three different datasets against other high performing
  algorithms. The results show that our method compares favourably to other methods
  across datasets.
authors:
- family-names: Iglesias-Martinez
  given-names: Luis F.
  email: luis.iglesiasmartinez@ucd.ie
  orcid: https://orcid.org/0000-0002-9110-2189
- family-names: Kegel
  given-names: Barbara
  name-particle: de
- family-names: Kolch
  given-names: Walter
repository: https://bioc.r-universe.dev
repository-code: https://github.com/Luisiglm/KBoost
url: https://github.com/Luisiglm/KBoost
contact:
- family-names: Iglesias-Martinez
  given-names: Luis F.
  email: luis.iglesiasmartinez@ucd.ie
  orcid: https://orcid.org/0000-0002-9110-2189