ArticleNature communications2019
Optimal control nodes in disease-perturbed networks as targets for combination therapy.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.Interdisciplinary sciences, computational life sciences · 2026Article
- A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction.Cell reports methods · 2026Article
- CTAS: a network control theory-based approach to identify key regulatory TFs of AS events during epithelial-mesenchymal transition.Briefings in bioinformatics · 2026Article
- Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.Animal cells and systems · 2025Review
- Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.Briefings in bioinformatics · 2024Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- Decoding the principle of cell-fate determination for its reverse control.NPJ systems biology and applications · 2024Article
- Advancements in pH-Responsive nanoparticles for osteoarthritis treatment: Opportunities and challenges.Frontiers in bioengineering and biotechnology · 2024Review
- BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo.PLoS computational biology · 2023Article
- WMDS.net: a network control framework for identifying key players in transcriptome programs.Bioinformatics (Oxford, England) · 2023Article
- AI-DrugNet: A network-based deep learning model for drug repurposing and combination therapy in neurological disorders.Computational and structural biotechnology journal · 2023Article
- Optimizing Genomic Control in Hit Network-Target Set Model Associations with Lung Adenocarcinoma.Journal of Cancer · 2023Article
- Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.Biology · 2022Article
- DSCN: Double-target selection guided by CRISPR screening and network.PLoS computational biology · 2022Article
- DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.Interdisciplinary sciences, computational life sciences · 2022Article
- SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.Database : the journal of biological databases and curation · 2022Article
- Construction of disease-specific cytokine profiles by associating disease genes with immune responses.PLoS computational biology · 2022Article
- Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.Frontiers in genetics · 2022Article
- Network Analysis Reveals Synergistic Genetic Dependencies for Rational Combination Therapy in Philadelphia Chromosome-Like Acute Lymphoblastic Leukemia.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.
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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.