ArticleBMC bioinformatics2016
A multiple kernel learning algorithm for drug-target interaction prediction.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 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
82 citing papers in PubMed, 236 citations in OpenAlex.
- Adaptive Self-Attention Graph Pooling for Drug-Target Affinity Prediction.International journal of molecular sciences · 2026Article
- Article
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- A novel method for drug-target affinity prediction by integrating predicted evolutionary information and multi-scale protein graphs.BMC biology · 2026Article
- ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data.Journal of chemical information and modeling · 2026Article
- Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism.BMC bioinformatics · 2025Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Benchmarking heterogeneous network-based methods for drug repurposing.NPJ systems biology and applications · 2025Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction.BMC plant biology · 2025Article
- SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.Bioinformatics (Oxford, England) · 2025Article
- DynHeter-DTA: Dynamic Heterogeneous Graph Representation for Drug-Target Binding Affinity Prediction.International journal of molecular sciences · 2025Article
- MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.Molecules (Basel, Switzerland) · 2025Article
- Drug-target Affinity Prediction by Molecule Secondary Structure Representation Network.Current medicinal chemistry · 2025Article
- An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.Current computer-aided drug design · 2025Article
- DTGHAT: multi-molecule heterogeneous graph transformer based on multi-molecule graph for drug-target identification.Frontiers in pharmacology · 2025Article
- Predicting drug protein interactions based on improved support vector data description in unbalanced data.BioImpacts : BI · 2025Article
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model.Scientific reports · 2024Article
- Leveraging multiple data types for improved compound-kinase bioactivity prediction.Nature communications · 2024Article
22 more citing papers are in PubMed but not listed here.
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information.
resultsWe propose KronRLS-MKL, which models the drug-target interaction problem as a link prediction task on bipartite networks. This method allows the integration of multiple heterogeneous information sources for the identification of new interactions, and can also work with networks of arbitrary size. Moreover, it automatically selects the more relevant kernels by returning weights indicating their importance in the drug-target prediction at hand. Empirical analysis on four data sets using twenty distinct kernels indicates that our method has higher or comparable predictive performance than 18 competing methods in all prediction tasks. Moreover, the predicted weights reflect the predictive quality of each kernel on exhaustive pairwise experiments, which indicates the success of the method to automatically reveal relevant biological sources.
conclusionsOur analysis show that the proposed data integration strategy is able to improve the quality of the predicted interactions, and can speed up the identification of new drug-target interactions as well as identify relevant information for the task. AVAILABILITY: The source code and data sets are available at www.cin.ufpe.br/~acan/kronrlsmkl/.
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.