ArticleAdvances in bioinformatics2013
An overview of the statistical methods used for inferring gene regulatory networks and protein-protein interaction networks.
Article in Advances in bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Uncovering Protein Networks in Cardiovascular Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Article
- Reverse engineering highlights potential principles of large gene regulatory network design and learning.NPJ systems biology and applications · 2017Article
- Stability indicators in network reconstruction.PloS one · 2014Article
- Root systems biology: integrative modeling across scales, from gene regulatory networks to the rhizosphere.Plant physiology · 2013Review
- Reverse engineering sparse gene regulatory networks using cubature kalman filter and compressed sensing.Advances in bioinformatics · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.
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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.