title: Inference and validation of predictive gene networks from biomedical literature and gene expression data.
abstract: 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.
authors.affiliation: Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium; Interuniversity Institute of Bioinformatics Brussels, ULB-VUB, La Plaine Campus, Brussels, Belgium.
authors.forename: Catharina
authors.initials: C
authors.lastname: Olsen
authors.affiliation: Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Fleming
authors.affiliation: Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
authors.forename: Niall
authors.initials: N
authors.lastname: Prendergast
authors.affiliation: Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
authors.forename: Renee
authors.initials: R
authors.lastname: Rubio
authors.affiliation: Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
authors.affiliation: Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium; Interuniversity Institute of Bioinformatics Brussels, ULB-VUB, La Plaine Campus, Brussels, Belgium.
authors.forename: Gianluca
authors.initials: G
authors.lastname: Bontempi
authors.affiliation: Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. Electronic address: bhaibeka@uhnresearch.ca.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haibe-Kains
authors.affiliation: Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA. Electronic address: johnq@jimmy.harvard.edu.
authors.forename: John
authors.initials: J
authors.lastname: Quackenbush
mesh_terms: D045744:Cell Line, Tumor; D015179:Colorectal Neoplasms / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D059467:Transcriptome; D054928:Validation Studies as Topic
keywords: Gene expression; Network inference; Quantitative validation; Targeted perturbations