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Privacy-preserving search for chemical compound databases

Overview of attention for article published in BMC Bioinformatics, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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1 news outlet
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1 X user

Citations

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10 Dimensions

Readers on

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37 Mendeley
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Title
Privacy-preserving search for chemical compound databases
Published in
BMC Bioinformatics, December 2015
DOI 10.1186/1471-2105-16-s18-s6
Pubmed ID
Authors

Kana Shimizu, Koji Nuida, Hiromi Arai, Shigeo Mitsunari, Nuttapong Attrapadung, Michiaki Hamada, Koji Tsuda, Takatsugu Hirokawa, Jun Sakuma, Goichiro Hanaoka, Kiyoshi Asai

Abstract

Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources. In order to overcome this dilemma, we developed a novel cryptographic protocol that enables database searching while keeping both the query holder's privacy and database holder's privacy. Generally, the application of cryptographic techniques to practical problems is difficult because versatile techniques are computationally expensive while computationally inexpensive techniques can perform only trivial computation tasks. In this study, our protocol is successfully built only from an additive-homomorphic cryptosystem, which allows only addition performed on encrypted values but is computationally efficient compared with versatile techniques such as general purpose multi-party computation. In an experiment searching ChEMBL, which consists of more than 1,200,000 compounds, the proposed method was 36,900 times faster in CPU time and 12,000 times as efficient in communication size compared with general purpose multi-party computation. We proposed a novel privacy-preserving protocol for searching chemical compound databases. The proposed method, easily scaling for large-scale databases, may help to accelerate drug discovery research by making full use of unused but valuable data that includes sensitive information.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Germany 1 3%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 22%
Researcher 6 16%
Student > Master 5 14%
Student > Ph. D. Student 5 14%
Professor > Associate Professor 2 5%
Other 7 19%
Unknown 4 11%
Readers by discipline Count As %
Computer Science 15 41%
Chemistry 4 11%
Agricultural and Biological Sciences 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Mathematics 2 5%
Other 7 19%
Unknown 4 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 March 2022.
All research outputs
#2,909,571
of 23,371,053 outputs
Outputs from BMC Bioinformatics
#990
of 7,394 outputs
Outputs of similar age
#50,097
of 391,815 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 154 outputs
Altmetric has tracked 23,371,053 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,394 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 391,815 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.