EnsInfer: a simple ensemble approach to network inference outperforms any single method

Research output: Contribution to journalArticlepeer-review


This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network inference methods vary in their performance across different datasets, so a method that works poorly on one dataset may work well on another; (ii) a non-homogeneous ensemble method in the form of a Naive Bayes classifier leads overall to as good or better results than using the best single base method or any other ensemble method; (iii) for the best results, the ensemble method should integrate all methods that satisfy a statistical test of normality on training data. The resulting ensemble model EnsInfer easily integrates all kinds of RNA-seq data as well as new and existing inference methods. The paper categorizes and reviews state-of-the-art underlying methods, describes the EnsInfer ensemble approach in detail, and presents experimental results. The source code and data used will be made available to the community upon publication.

Original languageEnglish (US)
Article number114
JournalBMC bioinformatics
Issue number1
StatePublished - Dec 2023


  • Gene regulatory networks
  • Machine learning
  • Non homogeneous ensemble
  • Transcriptional regulation

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


Dive into the research topics of 'EnsInfer: a simple ensemble approach to network inference outperforms any single method'. Together they form a unique fingerprint.

Cite this