ArticleFrontiers in genetics2020
A Machine Learning Method for Drug Combination Prediction.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Improving synergistic drug combination prediction with signature-based gene expression features in oncology.Frontiers in pharmacology · 2025Article
- iDOMO: identification of drug combinations via multi-set operations for treating diseases.Briefings in bioinformatics · 2024Article
- Harmonizing across datasets to improve the transferability of drug combination prediction.Communications biology · 2023Article
- MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.Bioinformatics (Oxford, England) · 2023Article
- PPDTS: Predicting potential drug-target interactions based on network similarity.IET systems biology · 2022Article
- Machine learning methods, databases and tools for drug combination prediction.Briefings in bioinformatics · 2022Article
- Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks.BMC bioinformatics · 2022Article
- PINet 1.0: A pathway network-based evaluation of drug combinations for the management of specific diseases.Frontiers in molecular biosciences · 2022Article
- GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19.Frontiers in immunology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-based methodologies are extremely costly in time and money. Many computational methods have been proposed to address these problems by starting from existing drug combinations. However, in most cases, only molecular structure information is included, which covers too limited a set of drug characteristics to efficiently screen drug combinations. Here, we integrated similarity-based multifeature drug data to improve the prediction accuracy by using the neighbor recommender method combined with ensemble learning algorithms. By conducting feature assessment analysis, we selected the most useful drug features and achieved 0.964 AUC in the ensemble models. The comparison results showed that the ensemble models outperform traditional machine learning algorithms such as support vector machine (SVM), naïve Bayes (NB), and logistic regression (GLM). Furthermore, we predicted 7 candidate drug combinations for a specific drug, paclitaxel, and successfully verified that the two of the predicted combinations have promising effects.
Indexed as
Identifiers
What OpenQuestion holds
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.