ArticleBMC systems biology2019
Network-based characterization of drug-protein interaction signatures with a space-efficient approach.
Article in BMC systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.Current drug discovery technologies · 2024Review
- Article
- Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review.International journal of molecular sciences · 2021Review
- OverCOVID: an integrative web portal for SARS-CoV-2 bioinformatics resources.Journal of integrative bioinformatics · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.Journal of cheminformatics · 2020Article
- Thompson loop: opportunities for antitubercular drug design by targeting the weak spot in demethylmenaquinone methyltransferase protein.RSC advances · 2020Article
- CovMulNet19, Integrating Proteins, Diseases, Drugs, and Symptoms: A Network Medicine Approach to COVID-19.Network and systems medicine · 2020Article
- Article
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Authors and funding
4 authors.
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Abstract
backgroundCharacterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology.
resultsWe present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call "drug-protein interaction signatures" from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers.
conclusionsOur method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions and are expected to contribute to rational drug design.
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