Extracting Protein-Protein Interaction Information from Biomedical Text with SVM

Tomohiro MITSUMORI; Masaki MURATA; Yasushi FUKUDA; Kouichi DOI; Hirohumi DOI

doi:10.1093/ietisy/e89-d.8.2464

Extracting Protein-Protein Interaction Information from Biomedical Text with SVM

Tomohiro MITSUMORI, Masaki MURATA, Yasushi FUKUDA, Kouichi DOI, Hirohumi DOI

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Automated information extraction systems from biomedical text have been reported. Some systems are based on manually developed rules or pattern matching. Manually developed rules are specific for analysis, however, new rules must be developed for each new domain. Although the corpus must be developed by human effort, a machine-learning approach automatically learns the rules from the corpus. In this article, we present a system for automatically extracting protein-protein interaction information from biomedical text with support vector machines (SVMs). We describe the performance of our system and compare its ability to extract protein-protein interaction information with that of other systems.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.8 pp.2464-2466

Publication Date: 2006/08/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.8.2464

Type of Manuscript: LETTER

Category: Natural Language Processing

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Tomohiro MITSUMORI, Masaki MURATA, Yasushi FUKUDA, Kouichi DOI, Hirohumi DOI, "Extracting Protein-Protein Interaction Information from Biomedical Text with SVM" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 8, pp. 2464-2466, August 2006, doi: 10.1093/ietisy/e89-d.8.2464.
Abstract: Automated information extraction systems from biomedical text have been reported. Some systems are based on manually developed rules or pattern matching. Manually developed rules are specific for analysis, however, new rules must be developed for each new domain. Although the corpus must be developed by human effort, a machine-learning approach automatically learns the rules from the corpus. In this article, we present a system for automatically extracting protein-protein interaction information from biomedical text with support vector machines (SVMs). We describe the performance of our system and compare its ability to extract protein-protein interaction information with that of other systems.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.8.2464/_p

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@ARTICLE{e89-d_8_2464,
author={Tomohiro MITSUMORI, Masaki MURATA, Yasushi FUKUDA, Kouichi DOI, Hirohumi DOI, },
journal={IEICE TRANSACTIONS on Information},
title={Extracting Protein-Protein Interaction Information from Biomedical Text with SVM},
year={2006},
volume={E89-D},
number={8},
pages={2464-2466},
abstract={Automated information extraction systems from biomedical text have been reported. Some systems are based on manually developed rules or pattern matching. Manually developed rules are specific for analysis, however, new rules must be developed for each new domain. Although the corpus must be developed by human effort, a machine-learning approach automatically learns the rules from the corpus. In this article, we present a system for automatically extracting protein-protein interaction information from biomedical text with support vector machines (SVMs). We describe the performance of our system and compare its ability to extract protein-protein interaction information with that of other systems.},
keywords={},
doi={10.1093/ietisy/e89-d.8.2464},
ISSN={1745-1361},
month={August},}

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TY - JOUR
TI - Extracting Protein-Protein Interaction Information from Biomedical Text with SVM
T2 - IEICE TRANSACTIONS on Information
SP - 2464
EP - 2466
AU - Tomohiro MITSUMORI
AU - Masaki MURATA
AU - Yasushi FUKUDA
AU - Kouichi DOI
AU - Hirohumi DOI
PY - 2006
DO - 10.1093/ietisy/e89-d.8.2464
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2006
AB - Automated information extraction systems from biomedical text have been reported. Some systems are based on manually developed rules or pattern matching. Manually developed rules are specific for analysis, however, new rules must be developed for each new domain. Although the corpus must be developed by human effort, a machine-learning approach automatically learns the rules from the corpus. In this article, we present a system for automatically extracting protein-protein interaction information from biomedical text with support vector machines (SVMs). We describe the performance of our system and compare its ability to extract protein-protein interaction information with that of other systems.
ER -