Evidence map›Paper›PMID 30893816›Full record

ArticleMolecules (Basel, Switzerland)2019

CuDDI: A CUDA-Based Application for Extracting Drug-Drug Interaction Related Substance Terms from PubMed Literature.

Yin Lu, Aditya Chandra Vothgod Ramachandra, Minh Pham, Yi-Cheng Tu, Feng Cheng

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yin LuDepartment of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL 33612, USA. yinlu86@gmail.com.
Aditya Chandra Vothgod RamachandraDepartment of Computer Science and Engineering, University of South Florida, Tampa, FL 33612, USA. adityachandr@mail.usf.edu.
Minh PhamDepartment of Mathematics and Statistics, University of South Florida, Tampa, FL 33620, USA. minhpham@mail.usf.edu.
Yi-Cheng TuDepartment of Computer Science and Engineering, University of South Florida, Tampa, FL 33612, USA. tuy@mail.usf.edu.
Feng ChengDepartment of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL 33612, USA. fcheng1@health.usf.edu.

Funding

Florida Department of Health 7AZ23University of South Florida PEG
6 · The paper itself

Abstract

Drug-drug interaction (DDI) is becoming a serious issue in clinical pharmacy as the use of multiple medications is more common. The PubMed database is one of the biggest literature resources for DDI studies. It contains over 150,000 journal articles related to DDI and is still expanding at a rapid pace. The extraction of DDI-related information, including compounds and proteins from PubMed, is an essential step for DDI research. In this paper, we introduce a tool, CuDDI (compute unified device architecture-based DDI searching), for identification of DDI-related terms (including compounds and proteins) from PubMed. There are three modules in this application, including the automatic retrieval of substances from PubMed, the identification of DDI-related terms, and the display of relationship of DDI-related terms. For DDI term identification, a speedup of 30⁻105 times was observed for the compute unified device architecture (CUDA)-based version compared with the implementation with a CPU-based Python version. CuDDI can be used to discover DDI-related terms and relationships of these terms, which has the potential to help clinicians and pharmacists better understand the mechanism of DDIs. CuDDI is available at: https://github.com/chengusf/CuDDI.

Indexed as

Drug InteractionsPubMedAlgorithmsData MiningHumansPublicationsSoftwareCUDAdrug-drug interactionGPUmechanismparallel computingPubMedrandom samplingSubstanceterm

Identifiers

PMID30893816
PMCPMC6470591

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.