Inference and validation of predictive gene networks from biomedical literature and gene expression data

Catharina Olsen; Kathleen Fleming; Niall Prendergast; Renee Rubio; Frank Emmert-Streib; Gianluca Bontempi; Benjamin Haibe-Kains; John Quackenbush

doi:10.1016/j.ygeno.2014.03.004

Inference and validation of predictive gene networks from biomedical literature and gene expression data

Genomics. 2014 May-Jun;103(5-6):329-36. doi: 10.1016/j.ygeno.2014.03.004. Epub 2014 Mar 29.

Authors

Catharina Olsen¹, Kathleen Fleming², Niall Prendergast², Renee Rubio², Frank Emmert-Streib³, Gianluca Bontempi¹, Benjamin Haibe-Kains⁴, John Quackenbush⁵

Affiliations

¹ Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium; Interuniversity Institute of Bioinformatics Brussels, ULB-VUB, La Plaine Campus, Brussels, Belgium.
² Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
³ Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK.
⁴ Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. Electronic address: [email protected].
⁵ Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA. Electronic address: [email protected].

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.

Keywords: Gene expression; Network inference; Quantitative validation; Targeted perturbations.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Cell Line, Tumor
Colorectal Neoplasms / genetics
Colorectal Neoplasms / metabolism
Gene Expression Profiling*
Gene Regulatory Networks*
Humans
Transcriptome
Validation Studies as Topic

Abstract

Publication types

MeSH terms

Grants and funding