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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

DISEASES : Text mining and data integration of disease-gene associations. / Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl.

In: Methods, Vol. 74, 03.2015, p. 83-9.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Pletscher-Frankild, S, Pallejà, A, Tsafou, K, Binder, JX & Jensen, LJ 2015, 'DISEASES: Text mining and data integration of disease-gene associations', Methods, vol. 74, pp. 83-9. https://doi.org/10.1016/j.ymeth.2014.11.020

APA

Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X., & Jensen, L. J. (2015). DISEASES: Text mining and data integration of disease-gene associations. Methods, 74, 83-9. https://doi.org/10.1016/j.ymeth.2014.11.020

Vancouver

Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. DISEASES: Text mining and data integration of disease-gene associations. Methods. 2015 Mar;74:83-9. https://doi.org/10.1016/j.ymeth.2014.11.020

Author

Pletscher-Frankild, Sune ; Pallejà, Albert ; Tsafou, Kalliopi ; Binder, Janos X ; Jensen, Lars Juhl. / DISEASES : Text mining and data integration of disease-gene associations. In: Methods. 2015 ; Vol. 74. pp. 83-9.

Bibtex

@article{150bf2c3cfba424fa2f9f94eab52bfe9,
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}",
note = "Copyright {\textcopyright} 2014. Published by Elsevier Inc.",
year = "2015",
month = mar,
doi = "10.1016/j.ymeth.2014.11.020",
language = "English",
volume = "74",
pages = "83--9",
journal = "Methods",
issn = "1046-2023",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - DISEASES

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

N1 - Copyright © 2014. Published by Elsevier Inc.

PY - 2015/3

Y1 - 2015/3

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.

U2 - 10.1016/j.ymeth.2014.11.020

DO - 10.1016/j.ymeth.2014.11.020

M3 - Journal article

C2 - 25484339

VL - 74

SP - 83

EP - 89

JO - Methods

JF - Methods

SN - 1046-2023

ER -

ID: 128737052