Conference proceeding
A mixture language model for class-attribute mining from biomedical literature digital library
2007 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, PROCEEDINGS, pp 174-182
01 Jan 2007
Abstract
We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of objects, (2) to discover latent attribute themes in the space of object descriptions, and (3) to summarize the commonalities and differences among identified classes along each attribute theme. We approach this mining problem through a mixture language model and estimate the parameters of the model using the EM algorithm. We demonstrate the effectiveness of the model with an application called protein community identification and annotation from Medline, the largest biomedical literature digital library with more than 16 millions abstracts.
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Details
- Title
- A mixture language model for class-attribute mining from biomedical literature digital library
- Creators
- Xiaohua Zhou - Drexel Univ, Coll Informat Sci & Technol, Data Mining & Bioinformat Lab, Philadelphia, PA 19104 USAXiaohua Hu - Drexel University, Information Science (Informatics)Xiaodan Zhang - Drexel Univ, Coll Informat Sci & Technol, Data Mining & Bioinformat Lab, Philadelphia, PA 19104 USADaniel D. Wu - Drexel Univ, Coll Informat Sci & Technol, Data Mining & Bioinformat Lab, Philadelphia, PA 19104 USA
- Publication Details
- 2007 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, PROCEEDINGS, pp 174-182
- Series
- IEEE International Conference on Bioinformatics and Biomedicine Workshop-BIBMW
- Publisher
- IEEE
- Number of pages
- 9
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000253368800024
- Scopus ID
- 2-s2.0-44949192814
- Other Identifier
- 991019173588804721
InCites Highlights
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- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic