ArticleFrontiers in genetics2014
Untangling statistical and biological models to understand network inference: the need for a genomics network ontology.
Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A multiscale model of the role of microenvironmental factors in cell segregation and heterogeneity in breast cancer development.PLoS computational biology · 2023Article
- Article
- Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data.Current genomics · 2019Article
- Extensive evaluation of the generalized relevance network approach to inferring gene regulatory networks.GigaScience · 2018Article
- Identifying latent dynamic components in biological systems.IET systems biology · 2015Article
- Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.Current genomics · 2015Article
- A Glimpse to Background and Characteristics of Major Molecular Biological Networks.BioMed research international · 2015Review
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Authors and funding
3 authors.
Funding
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Abstract
In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.
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Registered trials
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