Study of meta-analysis strategies for network inference using information-theoretic approaches
Tipo de documentoTexto en actas de congreso
Fecha de publicación2016
Condiciones de accesoAcceso abierto
Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a small number of samples. To date, there are mainly two strategies for the problem of interest: the first one (”data merging”) merges all datasets together and then infers a GRN whereas the other (”networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set of experiments based on in silico benchmarks. Furthermore, we present a new meta-analysis approach for inferring GRNs from multiple studies. Our proposed approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix.
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CitaciónPham, N C., Haibe-Kains, B., Bellot, P., Bontempi, G., Meyer, P. E. Study of meta-analysis strategies for network inference using information-theoretic approaches. A: International Workshop on Database and Expert Systems Applications. "2016 27th International Workshop on Database and Expert Systems Applications (DEXA)". Porto: 2016, p. 76-83.
Versión del editorhttp://ieeexplore.ieee.org/document/7816628/