ArticleBMC systems biology2011
A computational framework for gene regulatory network inference that combines multiple methods and datasets.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Multi-study inference of regulatory networks for more accurate models of gene regulation.PLoS computational biology · 2019Article
- Optimisation of an exemplar oculomotor model using multi-objective genetic algorithms executed on a GPU-CPU combination.BMC systems biology · 2017Article
- Computational prediction of molecular pathogen-host interactions based on dual transcriptome data.Frontiers in microbiology · 2015Article
- Data- and knowledge-based modeling of gene regulatory networks: an update.EXCLI journal · 2015Review
- A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.BMC bioinformatics · 2014Article
- Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients.BMC medical genomics · 2014Article
- A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.Microarrays (Basel, Switzerland) · 2014Article
- Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.PLoS computational biology · 2014Article
- Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns.BMC systems biology · 2014Article
- Article
- Article
- OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.Bioinformatics (Oxford, England) · 2013Article
- Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0.BMC systems biology · 2013Article
- Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.Genome research · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundReverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective.
resultsThis paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies.
conclusionsThe approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.
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.