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Bellot P.,Polytechnic University of Catalonia | Bellot P.,University of Liege | Olsen C.,Free University of Colombia | Olsen C.,Interuniversity Institute of Bioinformatics Brussels | And 3 more authors.
BMC Bioinformatics | Year: 2015

Background: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Results: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. Conclusions: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances. © 2015 Bellot et al.


Olsen C.,Free University of Colombia | Olsen C.,Interuniversity Institute of Bioinformatics Brussels | Fleming K.,Dana-Farber Cancer Institute | Prendergast N.,Dana-Farber Cancer Institute | And 6 more authors.
Genomics | Year: 2014

Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples. © 2014.


Olsen C.,Free University of Colombia | Olsen C.,Interuniversity Institute of Bioinformatics Brussels | Bontempi G.,Free University of Colombia | Bontempi G.,Interuniversity Institute of Bioinformatics Brussels | And 3 more authors.
Frontiers in Genetics | Year: 2014

When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F -scores. Furthermore, their integration with genomic data leads to a significant increase in F -scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two human tumor data sets. © 2014 Olsen, Bontempi, Emmert-Streib, Quackenbush and Haibe-Kains.

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