ArticleBioinformatics (Oxford, England)2016
Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Quantitative modeling of signaling in aggressive B cell lymphoma unveils conserved core network.PLoS computational biology · 2024Article
- Logical and experimental modeling of cytokine and eicosanoid signaling in psoriatic keratinocytes.iScience · 2021Article
- NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.Frontiers in genetics · 2021Article
- A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines.Frontiers in molecular biosciences · 2020Article
- Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects.Bioinformatics (Oxford, England) · 2019Article
- Single cell network analysis with a mixture of Nested Effects Models.Bioinformatics (Oxford, England) · 2018Article
- Improved pathway reconstruction from RNA interference screens by exploiting off-target effects.Bioinformatics (Oxford, England) · 2018Article
- DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Inferring modulators of genetic interactions with epistatic nested effects models.PLoS computational biology · 2017Article
- Refining Pathways: A Model Comparison Approach.PloS one · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationUnderstanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks.
resultsWe show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. AVAILABILITY AND IMPLEMENTATION: R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761. CONTACT: martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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.