ArticleJournal of cheminformatics2015
Optimizing drug-target interaction prediction based on random walk on heterogeneous networks.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Decoding non-coding SNPs: systems genomics modelling dissects the heterogeneity of IBD.Molecular systems biology · 2026Article
- PbsNRs: predict the potential binders and scaffolds for nuclear receptors.Briefings in bioinformatics · 2024Article
- SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.BMC bioinformatics · 2023Article
- Article
- Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery.Current genomics · 2021Review
- Drug-target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization.BMC bioinformatics · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network.Frontiers in genetics · 2021Article
- DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation.PloS one · 2021Article
- Drug Research Meets Network Science: Where Are We?Journal of medicinal chemistry · 2020Article
- DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.Journal of cheminformatics · 2020Article
- Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks.Journal of translational medicine · 2019Article
- Network inference with ensembles of bi-clustering trees.BMC bioinformatics · 2019Article
- edge2vec: Representation learning using edge semantics for biomedical knowledge discovery.BMC bioinformatics · 2019Article
- A unified solution for different scenarios of predicting drug-target interactions via triple matrix factorization.BMC systems biology · 2018Article
- SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.Journal of cellular and molecular medicine · 2018Article
- Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links.BMC bioinformatics · 2018Article
- RepTB: a gene ontology based drug repurposing approach for tuberculosis.Journal of cheminformatics · 2018Article
- DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting novel drug-target associations is important not only for developing new drugs, but also for furthering biological knowledge by understanding how drugs work and their modes of action. As more data about drugs, targets, and their interactions becomes available, computational approaches have become an indispensible part of drug target association discovery. In this paper we apply random walk with restart (RWR) method to a heterogeneous network of drugs and targets compiled from DrugBank database and investigate the performance of the method under parameter variation and choice of chemical fingerprint methods.
resultsWe show that choice of chemical fingerprint does not affect the performance of the method when the parameters are tuned to optimal values. We use a subset of the ChEMBL15 dataset that contains 2,763 associations between 544 drugs and 467 target proteins to evaluate our method, and we extracted datasets of bioactivity ≤1 and ≤10 μM activity cutoff. For 1 μM bioactivity cutoff, we find that our method can correctly predict nearly 47, 55, 60% of the given drug-target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 μM dataset in top 50 rank positions. For 10 μM bioactivity cutoff, we find that our method can correctly predict nearly 32.4, 34.8, 35.3% of the given drug-target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 μM dataset in top 50 rank positions. We further examine the associations between 110 popular top selling drugs in 2012 and 3,519 targets and find the top ten targets for each drug.
conclusionsWe demonstrate the effectiveness and promise of the approach-RWR on heterogeneous networks using chemical features-for identifying novel drug target interactions and investigate the performance.
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.