ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2011
Defining the players in higher-order networks: predictive modeling for reverse engineering functional influence networks.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections.Frontiers in cell and developmental biology · 2019Article
- Analysis of Protein Complexes in the Unicellular Cyanobacterium Cyanothece ATCC 51142.Journal of proteome research · 2018Article
- The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus.BMC systems biology · 2016Article
- A network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses.PloS one · 2013Article
- Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data.Expert opinion on medical diagnostics · 2013Article
- Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis.BMC systems biology · 2012Article
- Modeling dynamic regulatory processes in stroke.PLoS computational biology · 2012Article
- Article
- Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems.BMC systems biology · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics. We first develop a predictive network model of the transcriptomics of whole blood from a mouse model of neuroprotection in ischemic stroke, and show that it can accurately predict system behavior under novel conditions. We then use a network topology approach to expand the set of regulators considered and show that addition of topological bottlenecks improves the performance of the predictive model. Finally, we explore how improvements in definition of functional modules may be achieved through an integration of inferred network relationships and functional relationships defined using Gene Ontology similarity. We show that appropriate integration of these two types of relationships can result in models with improved performance.
Indexed as
Identifiers
What OpenQuestion holds
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.