ArticleNucleic acids research2013
Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- CANTATA-prediction of missing links in Boolean networks using genetic programming.Bioinformatics (Oxford, England) · 2022Article
- Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma.PLoS computational biology · 2019Article
- Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons.Genome research · 2017Article
- Proteome and Secretome Characterization of Glioblastoma-Derived Neural Stem Cells.Stem cells (Dayton, Ohio) · 2017Article
- Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.BMC bioinformatics · 2016Article
- Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation.BioEssays : news and reviews in molecular, cellular and developmental biology · 2016Review
- Reconstruction and Application of Protein-Protein Interaction Network.International journal of molecular sciences · 2016Review
- Prediction of disease-gene-drug relationships following a differential network analysis.Cell death & disease · 2016Article
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- Logical Modeling and Dynamical Analysis of Cellular Networks.Frontiers in genetics · 2016Review
- Integrated network model provides new insights into castration-resistant prostate cancer.Scientific reports · 2015Article
- TRIM32 modulates pluripotency entry and exit by directly regulating Oct4 stability.Scientific reports · 2015Article
- Constitutive activation of oncogenic PDGFRα-mutant proteins occurring in GIST patients induces receptor mislocalisation and alters PDGFRα signalling characteristics.Cell communication and signaling : CCS · 2015Article
- A differential network analysis approach for lineage specifier prediction in stem cell subpopulations.NPJ systems biology and applications · 2015Article
- Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.PloS one · 2015Article
- An extended gene protein/products Boolean network model including post-transcriptional regulation.Theoretical biology & medical modelling · 2014Article
- Gene regulatory network analysis reveals differences in site-specific cell fate determination in mammalian brain.Frontiers in cellular neuroscience · 2014Article
- Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes.Protein & cell · 2014Article
- A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.BioMed research international · 2014Review
- Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks.BMC systems biology · 2013Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions.
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