ArticleBioinformatics (Oxford, England)2020
Converting networks to predictive logic models from perturbation signalling data with CellNOpt.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces.NPJ systems biology and applications · 2026Article
- Control-guided refinement of partially specified Boolean networks: applications to RTK signaling.Bioinformatics (Oxford, England) · 2026Article
- Data driven network inference and longitudinal transcriptomics unveil dynamic regulation in Chronic Lymphocytic Leukaemia models.NPJ systems biology and applications · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- A highly annotated drug combination resource for catalyzing precision combinatorial therapy.Scientific data · 2025Article
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
- Transcriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD.Genes · 2024Article
- Augusta: From RNA-Seq to gene regulatory networks and Boolean models.Computational and structural biotechnology journal · 2024Article
- Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression.iScience · 2024Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Characterizing alternative splicing effects on protein interaction networks with LINDA.Bioinformatics (Oxford, England) · 2023Article
- Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.Frontiers in systems biology · 2023Article
- Functional selectivity of Receptor Tyrosine Kinases regulates distinct cellular outputs.Frontiers in cell and developmental biology · 2023Review
- Precision Combination Therapies Based on Recurrent Oncogenic Coalterations.Cancer discovery · 2022Article
- Boolean modelling as a logic-based dynamic approach in systems medicine.Computational and structural biotechnology journal · 2022Review
- Cluster Gauss-Newton and CellNOpt Parameter Estimation in a Small Protein Signaling Network of Vorinostat and Bortezomib Pharmacodynamics.The AAPS journal · 2021Article
- A practical guide to mechanistic systems modeling in biology using a logic-based approach.Briefings in bioinformatics · 2021Review
- In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
summaryThe molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large datasets is increasing steadily. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. CellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones to keep up with the computational demand of increasingly large datasets, including (i) an efficient integer linear programming, (ii) a probabilistic logic implementation for semi-quantitative datasets, (iii) the integration of a stochastic Boolean simulator, (iv) a tool to identify missing links, (v) systematic post-hoc analyses and (vi) an R-Shiny tool to run CellNOpt interactively. AVAILABILITY AND IMPLEMENTATION: R-package(s): https://github.com/saezlab/cellnopt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.