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Erschienen in:
Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Network Structure Versus Chemical Information in Drug-Drug Interaction Prediction

verfasst von : George Kefalas, Dimitrios Vogiatzis

Erschienen in: Complex Networks and Their Applications XI

Verlag: Springer International Publishing

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Abstract

We apply two embedding mechanisms, node2vec & mol2vec, on the problem of predicting Drug-Drug Interactions (DDIs). These mechanisms, respectively, convert drugs into vectors using the chemical information of the underlying chemical compound and the network information from the graph of drug interactions. Our goal is to compare Single Link Prediction models that are based on each embedding method by exploring the topological features of the drug interactions graph that make each approach more efficient in making correct predictions. We base our experiments on the DrugBank data set and use various computational chemistry tools such RDKit and PubChem, along with NetworkX, in order to create the chemical and structural embeddings for each drug.

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Vorheriges Kapitel Detection of Sparsity in Multidimensional Data Using Network Degree Distribution and Improved Supervised Learning with Correction of Data Weighting
Nächstes Kapitel Geometric Deep Learning Graph Pruning to Speed-Up the Run-Time of Maximum Clique Enumerarion Algorithms
Fußnoten
2
We consider the core difference of a sample interaction as the absolute difference of the k-core values of two nodes that are connected by the corresponding graph edge.
 
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Metadaten
Titel
Network Structure Versus Chemical Information in Drug-Drug Interaction Prediction
verfasst von
George Kefalas
Dimitrios Vogiatzis
Copyright-Jahr
2023
Buch
Complex Networks and Their Applications XI

Print ISBN: 978-3-031-21126-3

Electronic ISBN: 978-3-031-21127-0

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-21127-0_33

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