This paper is in the following e-collection/theme issue:

Theme Issue 2021: Health Natural Language Processing and Applications (16) Natural Language Processing (650) Ontologies, Classifications, and Coding (227)

Published on in Vol 9, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28277, first published .
Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study

Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study

Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study

Authors of this article:

Meng Wang1, 2 Author Orcid Image ;   Haofen Wang3 Author Orcid Image ;   Xing Liu4 Author Orcid Image ;   Xinyu Ma1 Author Orcid Image ;   Beilun Wang1 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

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  1. Kim J, Shin M. A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction. Applied Sciences 2023;13(5):2842 View
  2. Pavel A, Saarimäki L, Möbus L, Federico A, Serra A, Greco D. The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design. Computational and Structural Biotechnology Journal 2022;20:4837 View
  3. Rodríguez-Revello J, Barba-González C, Rybinski M, Navas-Delgado I. KNIT: Ontology reusability through knowledge graph exploration. Expert Systems with Applications 2023;228:120239 View
  4. Wu J, Ning Z, Ding Y, Wang Y, Peng Q, Fu L. KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations. Briefings in Bioinformatics 2023;24(5) View
  5. Cai L, Li J, Lv H, Liu W, Niu H, Wang Z. Integrating domain knowledge for biomedical text analysis into deep learning: A survey. Journal of Biomedical Informatics 2023;143:104418 View
  6. Alvarez-Mamani E, Dechant R, Beltran-Castañón C, Ibáñez A. Graph embedding on mass spectrometry- and sequencing-based biomedical data. BMC Bioinformatics 2024;25(1) View
  7. Dagenais S, Lee C, Cronenberger C, Wang E, Sahasrabudhe V. Proposing a framework to quantify the potential impact of pharmacokinetic drug–drug interactions caused by a new drug candidate by using real world data about the target patient population. Clinical and Translational Science 2024;17(3) View
  8. Hauben M, Rafi M, Abdelaziz I, Hassanzadeh O. Knowledge Graphs in Pharmacovigilance: A Scoping Review. Clinical Therapeutics 2024 View

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