Combining active learning and semi-supervised learning techniques to extract protein interaction sentences

Min Song; Hwanjo Yu; Wook Shin Han

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences

Min Song, Hwanjo Yu, Wook Shin Han

Department of Library and Information Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore integrating active learning approaches to semi-supervised SVMs with a NLP-driven feature selection technique. Our contributions in this paper are as follows: (a) We proposed a novel PPI extraction technique called PPISpotter by combining an active learning technique with semi-supervised SVMs to extract proteinprotein interaction. (b) We extracted a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques for SVM classifiers. (c) We conducted experiments with three different PPI corpora and showed that PPISpotter is superior to four other comparison techniques in terms of precision, recall, and F-measure.

Original language	English
Title of host publication	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	35-44
Number of pages	10
ISBN (Electronic)	9781605583020
Publication status	Published - 2010
Event	9th International Workshop on Data Mining in Bioinformatics, BIOKDD 2010, Held in Conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - Washington, United States Duration: 2010 Jul 25 → 2010 Jul 28

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other	9th International Workshop on Data Mining in Bioinformatics, BIOKDD 2010, Held in Conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Country/Territory	United States
City	Washington
Period	10/7/25 → 10/7/28

Bibliographical note

Publisher Copyright:
Copyright © 2010 ACM.

All Science Journal Classification (ASJC) codes

Software
Information Systems

Cite this

Song, M., Yu, H., & Han, W. S. (2010). Combining active learning and semi-supervised learning techniques to extract protein interaction sentences. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 35-44). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery.

Song, Min ; Yu, Hwanjo ; Han, Wook Shin. / Combining active learning and semi-supervised learning techniques to extract protein interaction sentences. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2010. pp. 35-44 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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title = "Combining active learning and semi-supervised learning techniques to extract protein interaction sentences",

abstract = "Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore integrating active learning approaches to semi-supervised SVMs with a NLP-driven feature selection technique. Our contributions in this paper are as follows: (a) We proposed a novel PPI extraction technique called PPISpotter by combining an active learning technique with semi-supervised SVMs to extract proteinprotein interaction. (b) We extracted a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques for SVM classifiers. (c) We conducted experiments with three different PPI corpora and showed that PPISpotter is superior to four other comparison techniques in terms of precision, recall, and F-measure.",

author = "Min Song and Hwanjo Yu and Han, {Wook Shin}",

note = "Publisher Copyright: Copyright {\textcopyright} 2010 ACM.; 9th International Workshop on Data Mining in Bioinformatics, BIOKDD 2010, Held in Conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining ; Conference date: 25-07-2010 Through 28-07-2010",

year = "2010",

language = "English",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery",

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Song, M, Yu, H & Han, WS 2010, Combining active learning and semi-supervised learning techniques to extract protein interaction sentences. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 35-44, 9th International Workshop on Data Mining in Bioinformatics, BIOKDD 2010, Held in Conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, United States, 10/7/25.

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences. / Song, Min; Yu, Hwanjo; Han, Wook Shin.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2010. p. 35-44 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Combining active learning and semi-supervised learning techniques to extract protein interaction sentences

AU - Song, Min

AU - Yu, Hwanjo

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AB - Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore integrating active learning approaches to semi-supervised SVMs with a NLP-driven feature selection technique. Our contributions in this paper are as follows: (a) We proposed a novel PPI extraction technique called PPISpotter by combining an active learning technique with semi-supervised SVMs to extract proteinprotein interaction. (b) We extracted a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques for SVM classifiers. (c) We conducted experiments with three different PPI corpora and showed that PPISpotter is superior to four other comparison techniques in terms of precision, recall, and F-measure.

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M3 - Conference contribution

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BT - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Song M, Yu H, Han WS. Combining active learning and semi-supervised learning techniques to extract protein interaction sentences. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2010. p. 35-44. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences

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