ReviewPhysical biology2011
Modeling information flow in biological networks.
Review in Physical biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- What Is the Trait d'Union between Retroactivity and Molecular Communication Performance Limits?Molecules (Basel, Switzerland) · 2022Article
- Integration of peripheral transcriptomics, genomics, and interactomics following trauma identifies causal genes for symptoms of post-traumatic stress and major depression.Molecular psychiatry · 2021Observational
- Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization.BMC medical informatics and decision making · 2020Article
- Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.PLoS computational biology · 2017Article
- Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.Cancer research · 2016Article
- Using biological networks to integrate, visualize and analyze genomics data.Genetics, selection, evolution : GSE · 2016Review
- Understanding Genotype-Phenotype Effects in Cancer via Network Approaches.PLoS computational biology · 2016Review
- Identifying and characterizing key nodes among communities based on electrical-circuit networks.PloS one · 2014Article
- Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model.Nucleic acids research · 2013Article
- A flood-based information flow analysis and network minimization method for gene regulatory networks.BMC bioinformatics · 2013Article
- Module cover - a new approach to genotype-phenotype studies.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2013Article
- CytoITMprobe: a network information flow plugin for Cytoscape.BMC research notes · 2012Article
- Bridging the Gap between Genotype and Phenotype via Network Approaches.Frontiers in genetics · 2012Article
- Chapter 5: Network biology approach to complex diseases.PLoS computational biology · 2012Article
- Identifying causal genes and dysregulated pathways in complex diseases.PLoS computational 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
4 authors at 2 institutions in 1 country.
Funding
Abstract
Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method.
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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.