Learning from mixture of experimental data: A constraint-based approach

Vincenzo Lagani, Ioannis Tsamardinos, Sofia Triantafillou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We propose a novel approach for learning graphical models when data coming from different experimental conditions are available. We argue that classical constraint-based algorithms can be easily applied to mixture of experimental data given an appropriate conditional independence test. We show that, when perfect statistical inference are assumed, a sound conditional independence test for mixtures of experimental data can consist in evaluating the null hypothesis of conditional independence separately for each experimental condition. We successively indicate how this test can be modified in order to take in account statistical errors. Finally, we provide "Proof-of-Concept" results for demonstrating the validity of our claims. © 2012 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages8
StatePublished - Jun 5 2012
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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