ArticleBioinformatics (Oxford, England)2010
Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 31 citations in OpenAlex.
- Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis.PloS one · 2019Article
- CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks.Frontiers in physiology · 2018Article
- Linear effects models of signaling pathways from combinatorial perturbation data.Bioinformatics (Oxford, England) · 2016Article
- How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.Journal of pathology informatics · 2016Article
- Newly Constructed Network Models of Different WNT Signaling Cascades Applied to Breast Cancer Expression Data.PloS one · 2015Article
- Reconstruction of the temporal signaling network in Salmonella-infected human cells.Frontiers in microbiology · 2015Article
- Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data.BMC bioinformatics · 2014Article
- Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines.BMC systems biology · 2014Article
- Article
- Spatial normalization of reverse phase protein array data.PloS one · 2014Article
- Article
- Considering unknown unknowns: reconstruction of nonconfoundable causal relations in biological networks.Journal of computational biology : a journal of computational molecular cell biology · 2013Article
- Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.PloS one · 2013Article
- Sparse Bayesian Graphical Models for RPPA Time Course Data.IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics · 2012Article
- Bayesian inference of signaling network topology in a cancer cell line.Bioinformatics (Oxford, England) · 2012Article
- Network inference using steady-state data and Goldbeter-Koshland kinetics. [corrected].Bioinformatics (Oxford, England) · 2012Article
- Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'.BMC bioinformatics · 2011Article
- A Bayesian hierarchical model for inference across related reverse phase protein arrays experiments.Journal of applied statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationNetwork modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays.
resultsSignalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway. AVAILABILITY: Dynamic deterministic effects propagation networks is implemented in the R programming language and available at http://www.dkfz.de/mga2/ddepn/.
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.