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Alkemio: association of chemicals with biomedical topics by text and data mining

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Item Type:Article
Title:Alkemio: association of chemicals with biomedical topics by text and data mining
Creators Name:Gijon-Correas, J.A. and Andrade-Navarro, M.A. and Fontaine, J.F.
Abstract:The PubMed(R) database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. Availability: http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio.
Keywords:Algorithms, Chemical Phenomena, Data Mining, Disease, Internet, Pharmaceutical Preparations, PubMed, Software
Source:Nucleic Acids Research
ISSN:0305-1048
Publisher:Oxford University Press (U.K.)
Volume:42
Number:W1
Page Range:W422-W429
Date:1 July 2014
Official Publication:https://doi.org/10.1093/nar/gku432
PubMed:View item in PubMed

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