ArticleCPT: pharmacometrics & systems pharmacology2015
A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose-Response Curve.
Article in CPT: pharmacometrics & systems pharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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
26 citing papers in PubMed.
- CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.Bioinformatics advances · 2026Article
- Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer drug combinations.Computational and structural biotechnology journal · 2025Article
- SynDRep: a synergistic partner prediction tool based on knowledge graph for drug repurposing.Bioinformatics advances · 2025Article
- Heterogeneous entity representation for medicinal synergy prediction.Bioinformatics (Oxford, England) · 2024Article
- Dual-view jointly learning improves personalized drug synergy prediction.Bioinformatics (Oxford, England) · 2024Article
- FDA-approved disulfiram as a novel treatment for aggressive leukemia.Journal of molecular medicine (Berlin, Germany) · 2024Article
- Predicting anticancer synergistic drug combinations based on multi-task learning.BMC bioinformatics · 2023Article
- COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming.Briefings in bioinformatics · 2023Article
- Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.Frontiers in systems biology · 2023Article
- NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction.International journal of molecular sciences · 2022Article
- A review of deep learning applications in human genomics using next-generation sequencing data.Human genomics · 2022Review
- Targeting the T-Cell Lymphoma Epigenome Induces Cell Death, Cancer Testes Antigens, Immune-Modulatory Signaling Pathways.Molecular cancer therapeutics · 2021Article
- Using Split Luciferase Assay and anti-HSV Glycoprotein Monoclonal Antibodies to Predict a Functional Binding Site Between gD and gH/gL.Journal of virology · 2021Article
- Anticancer drug synergy prediction in understudied tissues using transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021Review
- A Review of CurrentFrontiers in oncology · 2021Review
- Article
- Pharmacological Activation of Non-canonical NF-κB Signaling Activates Latent HIV-1 ReservoirsCell reports. Medicine · 2020Article
- DIGREM: an integrated web-based platform for detecting effective multi-drug combinations.Bioinformatics (Oxford, England) · 2019Article
- DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Gene expression data before and after treatment with an individual drug and the IC20 of dose-response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database.
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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.