ArticleMolecular systems biology2008
Models from experiments: combinatorial drug perturbations of cancer cells.
Article in Molecular systems biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
What it found
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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.
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Who cites it
102 citing papers in PubMed.
- GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes.Briefings in bioinformatics · 2025Article
- MADSP: predicting anti-cancer drug synergy through multi-source integration and attention-based representation learning.Bioinformatics (Oxford, England) · 2025Article
- AUC-PR is a More Informative Metric for Assessing the Biological Relevance of In Silico Cellular Perturbation Prediction Models.bioRxiv : the preprint server for biology · 2025Article
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
- AI-empowered perturbation proteomics for complex biological systems.Cell genomics · 2024Article
- Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways.Molecules (Basel, Switzerland) · 2023Review
- CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.Briefings in bioinformatics · 2023Article
- Analyzing the research landscape: Mapping frontiers and hot spots in anti-cancer research using bibliometric analysis and research network pharmacology.Frontiers in pharmacology · 2023Article
- Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.Frontiers in systems biology · 2023Article
- PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.Briefings in bioinformatics · 2022Article
- Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.Briefings in bioinformatics · 2021Review
- Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model.Bioinformatics (Oxford, England) · 2021Article
- Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.Methods in molecular biology (Clifton, N.J.) · 2021Article
- A systems biology approach to discovering pathway signaling dysregulation in metastasis.Cancer metastasis reviews · 2020Review
- Perturbation biology links temporal protein changes to drug responses in a melanoma cell line.PLoS computational biology · 2020Article
- Charting the Fragmented Landscape of Drug Synergy.Trends in pharmacological sciences · 2020Review
- Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins.Nature biotechnology · 2020Article
- Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network.BMC bioinformatics · 2019Article
- Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction.Bioinformatics (Oxford, England) · 2019Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.
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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.