ArticleNucleic acids research2021
Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 48 citations in OpenAlex.
- In-silico personalized protein-protein interaction networks prioritize candidate compounds for glioblastoma.PLoS computational biology · 2026Article
- Multimodal multiobjective optimization with structural network control principles to optimize personalized drug targets for drug discovery of individual patients.Briefings in bioinformatics · 2024Article
- Target controllability: a feed-forward greedy algorithm in complex networks, meeting Kalman's rank condition.Bioinformatics (Oxford, England) · 2024Article
- Integrating multi-omics data of childhood asthma using a deep association model.Fundamental research · 2024Article
- Systems biology approaches to identify driver genes and drug combinations for treating COVID-19.Scientific reports · 2024Article
- Development of the brain network control theory and its implications.Psychoradiology · 2024Review
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
- Total network controllability analysis discovers explainable drugs for Covid-19 treatment.Biology direct · 2023Article
- Cancer-keeper genes as therapeutic targets.iScience · 2023Article
- DeepTraSynergy: drug combinations using multimodal deep learning with transformers.Bioinformatics (Oxford, England) · 2023Article
- Total network controllability analysis discovers explainable drugs for Covid-19 treatment.Research square · 2023Article
- CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.Briefings in bioinformatics · 2023Article
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- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression.Journal of molecular cell biology · 2022Article
- Identifying the critical state of complex biological systems by the directed-network rank score method.Bioinformatics (Oxford, England) · 2022Article
- DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.Genomics, proteomics & bioinformatics · 2022Article
- Mitochondrial adaptation in cancer drug resistance: prevalence, mechanisms, and management.Journal of hematology & oncology · 2022Review
- Conserved Control Path in Multilayer Networks.Entropy (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.
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