The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Network link prediction by global silencing of indirect correlations
Open accessAbstract
By mathematically 'silencing' spurious, indirect correlations in networks, two groups devise approaches for improving many different types of network analyses. Predictions of physical and functional links between cellular components are often based on correlations between experimental measurements, such as gene expression. However, correlations are affected by both direct and indirect paths, confounding our ability to identify true pairwise interactions. Here we exploit the fundamental properties of dynamical correlations in networks to develop a method to silence indirect effects. The method receives as input the observed correlations between node pairs and uses a matrix transformation to turn the correlation matrix into a highly discriminative silenced matrix, which enhances only the terms associated with direct causal links. Against empirical data for Escherichia coli regulatory interactions, the method enhanced the discriminative power of the correlations by twofold, yielding >50% predictive improvement over traditional correlation measures and 6% over mutual information. Overall this silencing method will help translate the abundant correlation data into insights about a system's interactions, with applications ranging from link prediction to inferring the dynamical mechanisms governing biological networks.
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How difficult is inference of mammalian causal gene regulatory networks?
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Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model
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Metadata
- Authors
- Barzel, B., & Barabási, A.-L.
- Venue
- Nature Biotechnology
- Published
- 2013-07-14
- Volume
- 31
- Issue
- 8
- Pages
- 720-725
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2129794000
- Cited by
- 269
- References
- 11
- FWCI
- 10.89
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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