DISEASES: Text mining and data integration of disease-gene associations

Sune Pletscher-Frankild; Albert Pallejà; Kalliopi Tsafou; Janos X Binder; Lars Juhl Jensen

doi:10.1016/j.ymeth.2014.11.020

DISEASES: Text mining and data integration of disease-gene associations

Sune Pletscher-Frankild, Albert Pallejà, Kalliopi Tsafou, Janos X Binder, Lars Juhl Jensen

233 Citations (Scopus)

Abstract

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

Original language	English
Journal	Methods
Volume	74
Pages (from-to)	83-9
Number of pages	7
ISSN	1046-2023
DOIs	https://doi.org/10.1016/j.ymeth.2014.11.020
Publication status	Published - 1 Mar 2015

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title = "DISEASES: Text mining and data integration of disease-gene associations",

abstract = "Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.",

author = "Sune Pletscher-Frankild and Albert Pallej{\`a} and Kalliopi Tsafou and Binder, {Janos X} and Jensen, {Lars Juhl}",

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T2 - Text mining and data integration of disease-gene associations

AU - Pletscher-Frankild, Sune

AU - Pallejà, Albert

AU - Tsafou, Kalliopi

AU - Binder, Janos X

AU - Jensen, Lars Juhl

PY - 2015/3/1

Y1 - 2015/3/1

N2 - Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

AB - Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

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DO - 10.1016/j.ymeth.2014.11.020

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JO - Methods

JF - Methods

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DISEASES: Text mining and data integration of disease-gene associations

Abstract

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