ArticleBioinformatics (Oxford, England)2012
How networks change with time.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 45 citations in OpenAlex.
- Neighbor Affinity-Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks.Molecules (Basel, Switzerland) · 2017Article
- The structure of the yeast mitochondrial ribosome.Science (New York, N.Y.) · 2017Article
- BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes.PloS one · 2016Article
- How to train your microbe: methods for dynamically characterizing gene networks.Current opinion in microbiology · 2015Review
- Detecting temporal protein complexes from dynamic protein-protein interaction networks.BMC bioinformatics · 2014Article
- TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information.Bioinformatics (Oxford, England) · 2014Article
- Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines.BMC systems biology · 2014Article
- Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition.PloS one · 2014Article
- TimeXNet: identifying active gene sub-networks using time-course gene expression profiles.BMC systems biology · 2014Article
- Genotype to phenotype via network analysis.Current opinion in genetics & development · 2013Review
- Simple topological features reflect dynamics and modularity in protein interaction networks.PLoS computational biology · 2013Article
- Linking transcriptional changes over time in stimulated dendritic cells to identify gene networks activated during the innate immune response.PLoS computational biology · 2013Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
motivationBiological networks change in response to genetic and environmental cues. Changes are reflected in the abundances of biomolecules, the composition of protein complexes and other descriptors of the biological state. Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals.
resultsA new method predicts the dynamic state of protein complexes in a cell, with protein expression inferred from transcription profile time courses and protein complexes inferred by joint analysis of protein co-expression and protein-protein interaction maps. Two algorithmic advances are presented: a new method, DHAC (Dynamical Hierarchical Agglomerative Clustering), for clustering time-evolving networks; and a companion method, MATCH-EM, for matching corresponding clusters across time points. With link prediction as an objective assessment metric, DHAC provides a substantial advance over existing clustering methods. An application to the yeast metabolic cycle demonstrates how waves of gene expression correspond to individual protein complexes. Our results suggest regulatory mechanisms for assembling the mitochondrial ribosome and illustrate dynamic changes in the components of the nuclear pore. AVAILABILITY: All source code and data are available under the Boost Software License as supplementary material, at www.baderzone.org, and at sourceforge.net/projects/dhacdist.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.