ArticleEURASIP journal on bioinformatics & systems biology2008
Inference of Boolean networks using sensitivity regularization.
Article in EURASIP journal on bioinformatics & systems biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Attractor - a new turning point in drug discovery.Drug design, development and therapy · 2019Review
- Learning restricted Boolean network model by time-series data.EURASIP journal on bioinformatics & systems biology · 2014Article
- CaSPIAN: a causal compressive sensing algorithm for discovering directed interactions in gene networks.PloS one · 2014Article
- Recent development and biomedical applications of probabilistic Boolean networks.Cell communication and signaling : CCS · 2013Review
- Statistical inference and reverse engineering of gene regulatory networks from observational expression data.Frontiers in genetics · 2012Article
- RefNetBuilder: a platform for construction of integrated reference gene regulatory networks from expressed sequence tags.BMC bioinformatics · 2011Article
- Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks.EURASIP journal on bioinformatics & systems biology · 2011Article
- Constraint-based analysis of gene interactions using restricted boolean networks and time-series data.BMC proceedings · 2011Article
- Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inference of genetic regulatory networks from global measurements of gene expressions is an important problem in computational biology. Recent studies suggest that such dynamical molecular systems are poised at a critical phase transition between an ordered and a disordered phase, affording the ability to balance stability and adaptability while coordinating complex macroscopic behavior. We investigate whether incorporating this dynamical system-wide property as an assumption in the inference process is beneficial in terms of reducing the inference error of the designed network. Using Boolean networks, for which there are well-defined notions of ordered, critical, and chaotic dynamical regimes as well as well-studied inference procedures, we analyze the expected inference error relative to deviations in the networks' dynamical regimes from the assumption of criticality. We demonstrate that taking criticality into account via a penalty term in the inference procedure improves the accuracy of prediction both in terms of state transitions and network wiring, particularly for small sample sizes.
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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.