ArticleBMC bioinformatics2017
Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 42 citations in OpenAlex.
- Predicting unknown mammalian viral hosts with Dynamic Positive-Unlabeled learning.Nature communications · 2026Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- Dual graph convolutional neural network for predicting chemical networks.BMC bioinformatics · 2020Article
- DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions.BMC bioinformatics · 2019Article
- Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings.BMC bioinformatics · 2019Article
- Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.Briefings in bioinformatics · 2019Review
- Leveraging genetic interactions for adverse drug-drug interaction prediction.PLoS computational biology · 2019Article
- Positive-unlabelled learning of glycosylation sites in the human proteome.BMC bioinformatics · 2019Article
- Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning.PloS one · 2018Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInvestigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs.
resultsThe proposed method consists of three steps: i) application of Growing Self Organizing Maps to infer negatives from the unlabeled dataset; ii) using a pairwise similarity function to quantify the overlap between individual features of drugs and iii) using support vector machine classifier for inferring DDIs. We obtained 6036 DDIs from DrugBank database. Using the proposed approach, we inferred 589 drug pairs that are likely to not interact with each other; these drug pairs are used as representative data for the negative class in binary classification for DDI prediction. Moreover, we classify the predicted DDIs as Cytochrome P450 (CYP) enzyme-Dependent and CYP-Independent interactions invoking their locations on the Growing Self Organizing Map, due to the particular importance of these enzymes in clinically significant interaction effects. Further, we provide a case study on three predicted CYP-Dependent DDIs to evaluate the clinical relevance of this study.
conclusionOur proposed approach showed an absolute improvement in F1-score of 14 and 38% in comparison to the method that randomly selects unlabeled data points as likely negatives, depending on the choice of similarity function. We inferred 5300 possible CYP-Dependent DDIs and 592 CYP-Independent DDIs with the highest posterior probabilities. Our discoveries can be used to improve clinical care as well as the research outcomes of drug development.
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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.