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Citing this Article

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Published on 16.06.16 in Vol 5, No 2 (2016): Apr-Jun

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

Works citing "Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study"

According to Crossref, the following articles are citing this article (DOI 10.2196/resprot.5621):

(note that this is only a small subset of citations)

  1. Yildirim P, Kaya A. Clustering of Phentermine HCL Drug from Online Patient Medication Reviews. Procedia Computer Science 2019;151:1146
  2. Khosravi A, Kouhsar M, Goliaei B, Jayaram B, Masoudi-Nejad A. Systematic analysis of genes and diseases using PheWAS-Associated networks. Computers in Biology and Medicine 2019;109:311
  3. Ozsoy MG, Özyer T, Polat F, Alhajj R. Realizing drug repositioning by adapting a recommendation system to handle the process. BMC Bioinformatics 2018;19(1)
  4. Bollegala D, Maskell S, Sloane R, Hajne J, Pirmohamed M. Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach. JMIR Public Health and Surveillance 2018;4(2):e51
  5. Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. Binding site matching in rational drug design: algorithms and applications. Briefings in Bioinformatics 2018;
  6. Kruse RL, Vanijcharoenkarn K. Drug repurposing to treat asthma and allergic disorders: Progress and prospects. Allergy 2018;73(2):313
  7. Tutubalina E, Nikolenko S. Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews. Journal of Healthcare Engineering 2017;2017:1

According to Crossref, the following books are citing this article (DOI 10.2196/resprot.5621)

  1. Poroikov V, Druzhilovskiy D. In Silico Drug Design. 2019. :3
  2. Tutubalina E, Nikolenko S. Mining Intelligence and Knowledge Exploration. 2017. Chapter 17:174