ArticleBMC bioinformatics2016
Predicting drug target interactions using meta-path-based semantic network analysis.
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 41 papers.
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Who cites it
41 citing papers in PubMed.
- Multi-Modal Topology-Aware Graph Neural Network for Robust Chemical-Protein Interaction Prediction.International journal of molecular sciences · 2025Article
- LSA-DDI: Learning Stereochemistry-Aware Drug Interactions via 3D Feature Fusion and Contrastive Cross-Attention.International journal of molecular sciences · 2025Article
- TCoCPIn reveals topological characteristics of chemical protein interaction networks for novel feature discovery.Scientific reports · 2025Article
- Protein structural domain-disease association prediction based on heterogeneous networks.BMC genomics · 2025Article
- Hypothesizing mechanistic links between microbes and disease using knowledge graphs.Scientific reports · 2025Article
- DTGHAT: multi-molecule heterogeneous graph transformer based on multi-molecule graph for drug-target identification.Frontiers in pharmacology · 2025Article
- An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.Current computer-aided drug design · 2025Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- PbsNRs: predict the potential binders and scaffolds for nuclear receptors.Briefings in bioinformatics · 2024Article
- AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network.Briefings in bioinformatics · 2023Article
- A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug-Target Interaction Prediction.Molecules (Basel, Switzerland) · 2023Article
- Autophagy dark genes: Can we find them with machine learning?Natural sciences (Weinheim, Germany) · 2023Article
- Metapaths: similarity search in heterogeneous knowledge graphs via meta-paths.Bioinformatics (Oxford, England) · 2023Article
- DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.Journal of translational medicine · 2023Article
- Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.Scientific reports · 2022Article
- Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.Frontiers in genetics · 2022Article
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
- Article
- Application of network link prediction in drug discovery.BMC bioinformatics · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.
resultsSupervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction.
conclusionsThe proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time.
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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.