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Nat Biotechnol DOI:10.1038/nbt.2635

Network deconvolution as a general method to distinguish direct dependencies in networks.

Publication TypeJournal Article
Year of Publication2013
AuthorsFeizi, S, Marbach, D, Médard, M, Kellis, M
JournalNat Biotechnol
Date Published2013 Aug
KeywordsAlgorithms, Computational Biology, Computer Simulation, Gene Regulatory Networks, Models, Statistical, Oligonucleotide Array Sequence Analysis, Sequence Alignment, Signal Transduction, Software

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.


Alternate JournalNat. Biotechnol.
PubMed ID23851448
PubMed Central IDPMC3773370
Grant ListR01 HG004037 / HG / NHGRI NIH HHS / United States
RC1 HG005334 / HG / NHGRI NIH HHS / United States
RC2 HG005639 / HG / NHGRI NIH HHS / United States
HG005639 / HG / NHGRI NIH HHS / United States