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Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach

Publication ,  Conference
Dhami, DS; Yan, S; Kunapuli, G; Page, D; Natarajan, S
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2021

Most approaches for predicting drug-drug interactions (DDIs) have focused on text. We present the first work that uses multiple drug structure data - images, string representations and relationship representations. We exploit the recent advances in deep networks to integrate these varied sources of inputs in predicting DDIs. Our empirical evaluations clearly demonstrate the efficacy of combining heterogeneous data in predicting DDIs.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030772109

Publication Date

January 1, 2021

Volume

12721 LNAI

Start / End Page

252 / 257

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Dhami, D. S., Yan, S., Kunapuli, G., Page, D., & Natarajan, S. (2021). Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12721 LNAI, pp. 252–257). https://doi.org/10.1007/978-3-030-77211-6_28
Dhami, D. S., S. Yan, G. Kunapuli, D. Page, and S. Natarajan. “Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12721 LNAI:252–57, 2021. https://doi.org/10.1007/978-3-030-77211-6_28.
Dhami DS, Yan S, Kunapuli G, Page D, Natarajan S. Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 252–7.
Dhami, D. S., et al. “Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12721 LNAI, 2021, pp. 252–57. Scopus, doi:10.1007/978-3-030-77211-6_28.
Dhami DS, Yan S, Kunapuli G, Page D, Natarajan S. Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 252–257.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030772109

Publication Date

January 1, 2021

Volume

12721 LNAI

Start / End Page

252 / 257

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences