S
Stijn Meganck
Researcher at Vrije Universiteit Brussel
Publications - 43
Citations - 1422
Stijn Meganck is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Causal model & Bayesian network. The author has an hindex of 14, co-authored 43 publications receiving 1197 citations. Previous affiliations of Stijn Meganck include VU University Amsterdam & Université libre de Bruxelles.
Papers
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Journal ArticleDOI
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis
Cosmin Lazar,Jonatan Taminau,Stijn Meganck,David Steenhoff,Alain Coletta,Colin Molter,V. de Schaetzen,Robin Duque,Hugues Bersini,Ann Nowé +9 more
TL;DR: This survey focuses on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery, and presents them in a unified framework.
Journal ArticleDOI
Batch effect removal methods for microarray gene expression data integration: a survey
Cosmin Lazar,Stijn Meganck,Jonatan Taminau,David Steenhoff,Alain Coletta,Colin Molter,David Y. Weiss-Solis,Robin Duque,Hugues Bersini,Ann Nowé +9 more
TL;DR: Methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments are reviewed in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process.
Journal ArticleDOI
Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages
Jonatan Taminau,Stijn Meganck,Cosmin Lazar,David Steenhoff,Alain Coletta,Colin Molter,Robin Duque,Virginie de Schaetzen,David Y. Weiss Solís,Hugues Bersini,Ann Nowé +10 more
TL;DR: The newly released inSilicoMerging R/Bioconductor package allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets and enables researchers to fully explore the potential of combining gene expressionData for downstream analysis.
Journal Article
Learning Causal Bayesian Networks from Observations and Experiments : A Decision Theoretic Approach
TL;DR: An algorithm is introduced that allows to actively add results of experiments so that arcs can be directed during learning and it is shown that this approach allows to learn a causal Bayesian network optimally with relation to a number of decision criteria.
Journal ArticleDOI
Comparison of Merging and Meta-Analysis as Alternative Approaches for Integrative Gene Expression Analysis
TL;DR: Two different approaches for conducting large-scale analysis of microarray gene expression data—meta-analysis and data merging—are compared in the context of the identification of cancer-related biomarkers, by analyzing six independent lung cancer studies.