research-article

A Hypergraph-based Method for Pharmaceutical Data Similarity Retrieval

Authors:

Chen GuoAuthors Info & Claims

ICBDT '21: Proceedings of the 4th International Conference on Big Data Technologies

Pages 134 - 140

https://doi.org/10.1145/3490322.3490344

Published: 27 December 2021 Publication History

Abstract

Drug and compound similarity retrieval is very important for new drug research and development. Most pharmaceutical database provide keyword searching service. Because keyword search method cannot identify entity semantic similarity information, so the retrieval results often got poor drug or compound semantic similarity. In this paper, we propose an attribute semantic similarity retrieval method for pharmaceutical data based on hypergraph and natural language processing technology. Firstly, we use natural language processing technology to research and construct the drug attribute semantic similarity network. Then, we continue building a hypergraph based on drug attribute to get better retrieval efficiency. The experimental results show that, our method can provide similarity retrieval service for researchers.

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Cited By

Trajanov DTrajkovski VDimitrieva MDobreva JJovanovik MKlemen MŽagar ARobnik-Šikonja M(2023)Review of Natural Language Processing in PharmacologyPharmacological Reviews10.1124/pharmrev.122.00071575:4(714-738)Online publication date: 17-Mar-2023
https://doi.org/10.1124/pharmrev.122.000715

Index Terms

A Hypergraph-based Method for Pharmaceutical Data Similarity Retrieval
1. Applied computing

Index terms have been assigned to the content through auto-classification.

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ICBDT '21: Proceedings of the 4th International Conference on Big Data Technologies

September 2021

189 pages

ISBN:9781450385091

DOI:10.1145/3490322

Copyright © 2021 ACM.

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Publication History

Published: 27 December 2021

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ICBDT 2021

ICBDT 2021: 2021 4th International Conference on Big Data Technologies

September 24 - 26, 2021

Zibo, China

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Cited By

Trajanov DTrajkovski VDimitrieva MDobreva JJovanovik MKlemen MŽagar ARobnik-Šikonja M(2023)Review of Natural Language Processing in PharmacologyPharmacological Reviews10.1124/pharmrev.122.00071575:4(714-738)Online publication date: 17-Mar-2023
https://doi.org/10.1124/pharmrev.122.000715

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